Image neurography and diffusion anisotropy imaging

ABSTRACT

A neurography system (10) is disclosed for generating diagnostically useful images of neural tissue (i.e., neurograms) employing a modified magnetic resonance imaging system (14). In one embodiment, the neurography system selectively images neural tissue by employing one or more gradients to discriminate diffusion anisotropy in the tissue and further enhances the image by suppressing the contribution of fat to the image. The neurography system is part of a broader medical system (12), which may include an auxiliary data collection system (22), diagnostic system (24), therapeutic system (26), surgical system (28), and training system (30). These various systems are all constructed to take advantage of the information provided by the neurography system regarding neural networks, which information was heretofore unavailable.

This application is based upon an earlier filed U.K Patent ApplicationNo. 9301268.0, filed Jan. 22, 1993, which, in turn, is acontinuation-in-part of U.K. Patent Application No. 9216383.1, filedJul. 31, 1992, which, in turn, is a continuation-in-part of U.K. PatentApplication No. 9210810.9, filed May 21, 1992, which, in turn, is acontinuation-in-part of U.K. Patent Application No. 9209648.6, filed May5, 1992, which, in turn, is a continuation-in-part of U.K. PatentApplication No. 9207013.5, filed Mar. 30, 1992, which, in turn, is acontinuation-in-part of U.K. Patent Application No. 9205541.7, filedMar. 13, 1992, which, in turn, is a continuation-in-part of parent U.K.Patent Application No. 9205058.2, filed Mar. 9, 1992, the benefit of thefiling dates of which is hereby claimed pursuant to 35 U.S.C. §119.

FIELD OF THE INVENTION

The present invention relates generally to the field of imaging and,more particularly, to the imaging of nerve tissue and otherdiffusionally anisotropic structures.

BACKGROUND OF THE INVENTION

Although many techniques have been developed for locating and viewingthe brain, spinal cord, and spinal roots within the spinal canal,hitherto there has not been a successful method for viewing theperipheral, autonomic, and cranial nerves. These nerves, collectivelyreferred to herein as peripheral nerves, commonly travel through andalong bone, muscle, lymphatics, tendons, ligaments, intermuscular septa,collections of fatty tissues, air and fluid spaces, veins, arteries,joints, skin, mucous membranes and other tissues. The relatively smallsize of peripheral nerves, as well as their close proximity to othertissue of comparable size and shape, makes them difficult to locate andidentify.

The examination of peripheral nerves is further complicated by thecomplexity of many such neural structures, such as the brachial plexus,lumbar plexus, and sacral plexus. These structures include bundles ofnerves that may join together, separate, rejoin, intermix, andresegregate, forming intricate three dimensional patterns. A compressionor irritation of a small area of nerve within such a plexus (e.g. in theshoulder) can cause pain, numbness, weakness or paralysis at somedistant site (e.g. in one finger). Even when a surgeon attempts toexpose the plexus for direct inspection, the anatomic complexity canprove overwhelming, rendering diagnosis inconclusive and surgerydifficult and dangerous.

Radiologic methods employing, for example, X-rays, have been developedto generate tissue specific images of various physiological structuresincluding bone, blood vessels, lymphatics, the gastrointestinal tract,and the tissues of the central nervous system. Due in part to the neuralcharacteristics noted above, however, these techniques have not beensuccessfully used to generate suitable clinical images of peripheralnerves.

Typically, the position of peripheral nerves in radiologic images hasbeen inferred by reference to more conspicuous, non-neural structuressuch as tendons, vessels, or bone. For example, by producing an X-rayimage of a region of the body through which a nerve of interest passes,non-neural structures can often be readily identified. Then, thelocations of peripheral nerves in the region can be inferred fromstandard reference information about human anatomy. Due to thevariability of nerve position from one individual to another, however,this technique is of limited value.

One approach of particular interest that has been used to imagephysiological structures is magnetic resonance imaging (MRI). By way ofintroduction, MRI involves the exposure of tissue to a variety ofdifferent magnetic and radio-frequency (rf) electromagnetic fields. Theresponse of the specimen's atomic nuclei to the fields is then processedto produce an image of the specimen.

More particularly, the specimen is initially exposed to a polarizingmagnetic field. In the presence of this field, nuclei exhibitingmagnetic moments (hereinafter referred to as spins) will seek to alignthemselves with the field. The nuclei precess about the polarizing fieldat an angular frequency (hereinafter referred to as the Larmorfrequency) whose magnitude depends upon both the field's strength andthe magnetogyric constant of the specific nuclear species involved.

Although the magnetic components of the spins cancel each other in aplane perpendicular to the polarizing field, the spins exhibit a netmagnetic moment in the direction of the polarizing field. By applying anexcitation field perpendicular to the polarizing field and at afrequency near the Larmor frequency, the net magnetic moment can betilted. The tilted magnetic moment includes a transverse component, inthe plane perpendicular to the polarizing field, rotating at the Larmorfrequency. The extent to which the magnetic moment is tilted and, hence,the magnitude of the net transverse magnetic moment, depends upon themagnitude and duration of the excitation field.

An external return coil is used to sense the field associated with thetransverse magnetic moment, once the excitation field is removed. Thereturn coil, thus, produces a sinusoidal output, whose frequency is theLarmor frequency and whose amplitude is proportional to that of thetransverse magnetic moment. With the excitation field removed, the netmagnetic moment gradually reorients itself with the polarizing field. Asa result, the amplitude of the return coil output decays exponentiallywith time.

Two factors influencing the rate of decay are known as the spin-latticerelaxation coefficient T₁ and the spin-spin relaxation coefficient T₂.The spin-spin relaxation coefficient T₂ represents the influence thatinteractions between spins have on decay, while the spin-latticerelaxation coefficient T₁ represents the influence that interactionsbetween spins and fixed components have on decay. Thus, the rate atwhich the return coil output decays is dependent upon, and indicativeof, the composition of the specimen.

By employing an excitation field that has a narrow frequency band, onlya relatively narrow band within a nuclear species will be excited. As aresult, the transverse magnetic component and, hence, return coiloutput, will exhibit a relatively narrow frequency band indicative ofthat band of the nuclear species. On the other hand, if the excitationfield has a broad frequency band, the return coil output may includecomponents associated with the transverse magnetic components of agreater variety of frequencies. A Fourier analysis of the output allowsthe different frequencies, which can be indicative of different chemicalor biological environments, to be distinguished.

In the arrangement described above, the contribution of particular spinsto the return coil output is not dependent upon their location withinthe specimen. As a result, while the frequency and decay of the outputcan be used to identify components of the specimen, the output does notindicate the location of components in the specimen.

To produce such a spatial image of the specimen, gradients areestablished in the polarizing field. The direction of the polarizingfield remains the same, but its strength varies along the x, y, and zaxes oriented with respect to the specimen. By varying the strength ofthe polarizing field linearly along the x-axis, the Larmor frequency ofa particular nuclear species will also vary linearly as a function ofits position along the x-axis. Similarly, with magnetic field gradientsestablished along the y-axis and z-axis, the Larmor frequency of aparticular species will vary linearly as a function of its positionalong these axes.

As noted above, by performing a Fourier analysis of the return coil'soutput, the frequency components of the output can be separated. With anarrow band excitation field applied to excite a select nuclear species,the position of a spin relative to the xyz coordinate system can then bedetermined by assessing the difference between the coil output frequencyand the Larmor frequency for that species. Thus, the MRI system can beconstructed to analyze frequency at a given point in time to determinethe location of spins relative to the magnetic field gradients and toanalyze the decay in frequency to determine the composition of thespecimen at a particular point.

The generation and sensing of the fields required for proper operationof an MRI system is achieved in response to the sequential operation of,for example, one or more main polarizing field coils, polarizinggradient field coils, rf excitation field coils, and return field coils.Commonly, the same coil arrangement is used to generate the excitationfield and sense the return field. A variety of different sequences havebeen developed to tailor specific aspects of MRI system operation, asdescribed, for example, in U.S. Pat. No. 4,843,322 (Glover); U.S. Pat.No. 4,868,501 (Conolly); and U.S. Pat. No. 4,901,020 (Ladebeck et al.).

One application of conventional MRI systems is in the production ofangiograms, or blood vessel images. Various different pulse sequencesand processing techniques have been developed for use in MRIangiography, as described in, for example, U.S. Pat. No. 4,516,582(Redington); U.S. Pat. No. 4,528,985 (Macovski); U.S. Pat. No. 4,647,857(Taber); U.S. Pat. No. 4,714,081 (Dumoulin et al.); U.S. Pat. No.4,777,957 (Wehrli et al.); and U.S. Pat. No. 4,836,209 (Nishimura).

As will be appreciated, blood vessels are readily differentiated fromsurrounding tissue by the pulsatile flow of blood therethrough. MRIangiography exploits this distinguishing characteristic to generateimages of the blood vessels in various ways. For example, if theexcitation field is pulsed at systole and diastole, the contribution ofblood flow to the return field will differ, while the contribution ofstatic tissue and bone to the return field will be the same. Bysubtracting one return from the other, the static component cancels,leaving only the contribution from the blood vessel.

Unfortunately, because peripheral nerve does not exhibit theflow-distinctiveness of blood vessels, MRI angiography systems and pulsesequences can not be used to generate suitable images of peripheralnerve. Further, conventional MRI systems and sequences used for generalimaging of tissue and bone do not provide acceptable results. Given thepoor signal-to-noise (S/N) ratio of the return signals (e.g., on theorder of 1× to 1.5×) and the small size of the nerve, the conspicuity ofimaged nerves relative to other tissue is collectively rendered so pooras to be diagnostically useless.

One technique proposed for use in enhancing the utility of MRI systemsin imaging neural tissue involves the use of pharmaceutical agents toenhance the contrast of neural tissue relative to surrounding tissue inthe images produced. As described in PCT Patent Application No. PCT EP91/01780 (Filler et al., WO 92/04916), published on Apr. 2, 1992, atwo-part contrast agent, such as wheat germ agglutinin ordextrin-magnetite, is injected so that it is subsequently taken up, andtransported, by the nerve of interest. The first part of the agentpromotes neural uptake, while the second part of the agent has thedesired "imageable" property.

The agent is injected into muscle and undergoes axoplasmic flow in thenerve supplying that muscle, tagging the nerve in subsequently generatedimages of the specimen. If MRI is used, the second part of the agent isselected to have a magnetically active (e.g., ferrite) component. Anagent having a high nuclear density can, however, be used to increasethe contrast of the nerve upon X-ray or computed tomography (CT)examination, while a radioactive (e.g. positron emitting) element can beused to enhance visibility during positron emission tomography (PET)scanning.

To illustrate the effectiveness of contrast agents in imaging nerve,reference is had to FIGS. 1-5. In that regard, FIG. 1 is a diagram of atransverse section of the upper forearm FA of a rabbit. The forearmincludes the triceps muscle TM, ulnar nerve UN, brachial veins BV,median nerve MN, radial nerve RN, humerus H, cephalic vein CV, andbiceps muscle BM.

FIGS. 2A and 2B illustrate spin-echo MR images of such a section, usinga ferrite contrast agent, produced by a conventional MRI system atsix-hour intervals. Although some of the larger structural elements arereadily identified, the location of some objects appears skewed. Moreparticularly, the humerus marrow appears shifted relative to the humerusH, as do ligaments L, and fat F between the biceps or triceps. Inaddition, smaller neural structures are difficult to distinguish.

Several approaches are available, however, to attempt to identify nervesin the images generated. For example, as shown in FIG. 3, if a short tauinversion recovery (STIR) sequence of the type described in Atlas etal., STIR MR Imaging of the Orbit, 151 AM. J. ROENTGEN. 1025-1030 (1988)is used, the humerus marrow disappears from the image as does, moreimportantly, certain ambiguous, apparently non-neural structuresadjacent the median nerve MN. Thus, as shown in the enlarged image ofthe region including the median nerve MN and ulnar nerve UN, provided inFIG. 4, the median nerve MN is visible.

Similarly, even when the contrast agent images of FIGS. 2A and 2B areenlarged to better illustrate the region including the median nerve MN,as shown in FIGS. 5A and 5B, respectively, the nerves aredistinguishable to a highly skilled observer. More particularly,transport of the ferrite contrast agent during the six-hour intervalbetween the generation of images 4A and 4B results in a loss ofintensity in the MN relative to the non-neural structure adjacent mediannerve MN. Given this observation and the STIR-based assessment, themedian nerve MN can, thus, be identified.

The use of contrast agents, while promising, does have certainlimitations. For example, there is an increasing preference to avoid theuse of invasive technologies in medicine whenever possible. Further,contrast agents generally can be used to image only a single nerve ornerve group. Of perhaps greatest importance, the contrast agentsemployed typically reduce the intensity of the imaged nerve. Sincenerves are already difficult to see in current MRI images, the impact ofthe contrast agent upon the image can be difficult to interpret, asillustrated by the discussion of FIGS. 2-5 above.

In another application, MRI has been used, without contrast agents, tomap non-peripheral, white matter nerve tracts in the brain. The whitematter tracts extend through gray matter tissue in the brain and exhibitrelatively high anisotropic diffusion. More particularly, given theirphysical structure (i.e., axonal pathways surrounded by myelin sheaths),water mobility along the white matter tracts is relatively high, whilewater mobility perpendicular to the tracts is low. The surrounding graymatter does not, however, exhibit this same anisotropy.

A technique for MRI-based mapping of white matter nerve tracts thatexploits this characteristic of neural tissue is described in Douek etal., Myelin Fiber Orientation Color Mapping, BOOK OF ABSTRACTS, SOCIETYOF MAGNETIC RESONANCE IN MEDICINE, p. 910 (1991). Basically, in additionto the fields and gradients described above, this process involves theuse of a pair of field gradient pulses (hereinafter referred to asdiffusion gradients), oriented perpendicular and parallel to the whitematter tracts to be imaged. The effect of a pulsed gradient is to changethe phase of the received signal from all of the spins. For stationaryspins the effect of the two diffusion gradients cancels out. Incontrast, spins moving from one spatial position to another in the timebetween the two diffusion gradients experience changes in the frequencyand phase of the spin magnetization with the net effect being areduction in the received signal. The signal reduction is greatest forthose spins that diffuse the greatest distance between the two pulsedgradients.

As noted above, given the anisotropic nature of the tracts, water willdiffuse freely along a tract, but is restricted in it motionperpendicular to the tract. When the diffusion gradient is aligned withthe tract there is thus a greater reduction in signal than when thediffusion gradient is aligned perpendicular to the tract. Because thisphenomenon is not exhibited by the surrounding gray matter tissue, thewhite matter tracts can be identified.

Anisotropic diffusion is also a recognized characteristic of peripheralnerve, as indicated in Moseley et al., Anisotropy in Diffusion-WeightedMRI, 19 MAGNETIC RESONANCE ON MEDICINE 321 (1991). The Douek et al.technology, however, does not distinguish peripheral nerve from muscleand other tissue for a number of previously unrecognized reasons. First,while the size and structure of the white matter tracts ensure that theresultant signals will be sufficiently strong for imaging, peripheralnerve is considerably smaller and more difficult to distinguish. Second,unlike the white matter tracts, peripheral nerve is commonly surroundedby muscle and fat, both of which impair the ability of the Douek et al.system to image nerve.

By way of elaboration, given its fibrous structure, muscle also exhibitsdiffusional anisotropy, as recognized in Moseley et al., Acute Effectsof Exercise on Echo-Planar T₂ and Diffusion-Weighted MRI of SkeletalMuscle in Volunteers, BOOK OF ABSTRACTS, SOCIETY OF MAGNETIC RESONANCEIN MEDICINE 108 (1991). As a result, the simple anisotropic analysis ofDouek et al. is unable to distinguish peripheral nerve and muscle. Whilefat is isotropic and, therefore, distinguishable from nerve, it alsoimpairs the imaging of peripheral nerves. Specifically, the relativesignal strength of fat returns to neural returns is so high as to renderperipheral nerves unidentifiable in images produced.

As will be appreciated from the preceding remarks, it would be desirableto develop a method for rapidly and non-invasively imaging a singleperipheral nerve, or an entire neural network, without resort tocontrast agents. The images generated should be sufficiently detailedand accurate to allow the location and condition of individualperipheral nerves to be assessed. It would further be desirable toprovide a system that processes neural images to enhance the informationcontent of the images, diagnose neural trauma and disorders, and informand control the administration of treatments and therapy.

SUMMARY OF THE INVENTION

The present disclosure relates to a new method, which quite remarkably,is capable of generating a three dimensional image of an individualpatient's nerves and nerve plexuses. The image can be acquirednon-invasively and rapidly by a magnetic resonance scanner. These imagesare acquired in such a way that some embodiments of the invention areable to make all other structures in the body including bone, fat, skin,muscle, blood, and connective tissues tend to disappear so that only thenerve tree remains to be seen. A plurality of the nerves passing througha given imaged region may be observed simultaneously, thus alleviatingany ambiguity of nerve identification which might arise were only asingle nerve imaged as with some contrast agent techniques.

The invention is based on the discovery of a method of collecting a dataset of signal intensities with spatial coordinates which describes thepositions of the nerves within any two dimensional cross section of aliving mammal or within any three dimensional data acquisition space.There exist a large number of pulse sequences capable of controlling oroperating a magnetic resonance imaging apparatus and each of whichaccomplishes some preferred image optimization. Previously, however, nosimple (single) or complex (double or multiple) pulse sequence has beenable to increase the relative signal intensity of nerve so that it isbrighter than all other tissues in the body or limb cross section.Surprisingly, the inventors have discovered that there are certain novelways of assembling complex pulse sequences, wherein even though thesimple components of the sequence decrease the signal-to-noise ratio ofnerve or decrease the signal strength of nerve relative to othertissues, the fully assembled complex sequence actually results in thenerve signal being more intense than any other tissue. In this fashion,the image conspicuity of nerve is greatly increased.

Thus, a first aspect of the present invention provides a method ofselectively imaging neural tissue of a subject without requiring use ofintraneural contrast agents, the method comprising subjecting part ofthe subject anatomy to magnetic resonance imaging fields, detectingmagnetic resonance and producing an image of neural tissue from saiddetected resonance so that a nerve, root, or neural tract of interest insaid image can be visually differentiated from surrounding structures.

A second aspect of the present invention provides a method ofselectively imaging neural tissue of a subject, the method comprisingsubjecting part of the subject anatomy to magnetic resonance imagingfields adapted to discriminate anisotropy of water diffusion or otherspecial characteristic of neural tissue, detecting magnetic resonance toproduce an electronic signal in accordance with said resonance andproducing an image of neural tissue from said electronic signal.

The invention also provides an apparatus for selectively imaging neuraltissue of a subject without requiring the use of neural contrast agents,the apparatus comprising means for subjecting part of the subjectanatomy to magnetic resonance fields, means for detecting magneticresonance to produce an electronic signal in accordance with saidresonance, and means for producing an image of neural tissue from saidelectronic signal so that a nerve, root, or neural tract of interest insaid image can be visually differentiated from surrounding structures.

The invention also finds expression as an apparatus for imaging neuraltissue of a subject, the apparatus comprising means for subjecting partof the subject anatomy to magnetic resonance fields adapted todiscriminate anisotropy of water diffusion, means for detecting magneticresonance to produce an electronic signal in accordance with saidresonance and means for producing a selective image of neural tissue ofinterest from said electronic signal.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages of thisinvention will become more readily appreciated as the same becomesbetter understood by reference to the following detailed description,when taken in conjunction with the accompanying drawings, wherein:

FIG. 1 is a diagram of a transverse section of the upper forearm of arabbit illustrating various neural and non-neural structures;

FIGS. 2A and 2B are images of the upper forearm of a rabbit, of the typedepicted in FIG. 1, produced using an MRI system at two spaced-aparttimes after the forearm was injected with a ferrite contrast agent;

FIG. 3 is another image of the upper forearm of a rabbit produced usingan MRI system employing a short tau inversion recovery (STIR) spin-echosequence;

FIG. 4 is an enlargement of a portion of the image of FIG. 3 associatedwith a peripheral nerve of interest;

FIGS. 5A and 5B are enlargements of a portion of the images of FIGS. 2Aand 2B, respectively, associated with a peripheral nerve of interest;

FIG. 6 is a block diagram of a neurography system, constructed inaccordance with this invention, coupled to a plurality of other systemsdesigned to provide information to the neurography system and toimplement, for example, neural diagnoses, therapy, surgery, andtraining;

FIG. 7 is a functional chart of the operation of the neurography systemof FIG. 6;

FIG. 8 is an illustration of the various components included in theneurography system of FIG. 6;

FIGS. 9 and 10 are flow charts depicting one way in which theneurography system of FIG. 8 may be used to generate neurograms;

FIGS. 11A through 11F illustrate one sequence of pulses suitable for usein producing diagnostically suitable images from the neurography systemof FIG. 6;

FIG. 12 is another image of the upper forearm of a rabbit produced by anembodiment of the neurography system employing fat suppression;

FIGS. 13A and 13B are additional images of the upper forearm of a rabbitproduced by an embodiment of the neurography system employing gradientsperpendicular and parallel, respectively, to the anisotropic axis ofnerve being imaged;

FIGS. 14A through 14D are images of the upper forearm of a rabbitproduced employing gradients of 0, 3, 5, and 7 Gauss/centimeter,respectively;

FIGS. 15A through 15C are images produceable by the neurography systemwith zero, perpendicular, and parallel gradients, while FIG. 15D is animage based upon the images of FIGS. 15B and 15C, referred to herein asa subtraction neurogram

FIG. 16 vector length image of the brain produced using the neurographysystem of FIG. 8;

FIG. 17 is an arctan image of the brain produced using the neurographysystem of FIG. 8;

FIGS. 18A through 18D are images of a rabbit forearm produced using theneurography system of FIG. 8, and illustrating the influence of the TEsequence upon the images produced;

FIG. 19 illustrates a splint employed in the neurography and medicalsystems of the present invention;

FIGS. 20, 21, and 22 are illustrations of images of a human sciaticnerve produced using the neurography system of FIG. 8, with FIGS. 20 and21 illustrating the ability of the system to image nerve fascicles (intwo cross-sectional scales) and FIG. 22 illustrating an axial projectionof the nerve;

FIG. 23 is a diagram of a cross-section of a vertebra, illustrating thetypes of structure present in one neurography application; and

FIG. 24 is a schematic illustration of a surgical system constructed inaccordance with this invention for use with the neurography system ofFIG. 8.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring now to FIG. 6, a neurography system 10 is shown as onecomponent of a broader medical system 12. Unlike prior art arrangements,system 10 quickly and non-invasively generates accurate images showingthe pattern of individual peripheral nerves, or entire nerve trees,without the use of contrast agents. The system is designed to allow suchimages, hereinafter referred to as neurograms, to be displayed intwo-dimensions, illustrating neural cross sections in the specimen underexamination, or in three-dimensions. The images may selectively excludeall other structures within the specimen, or may illustrate the physicalrelationship of other structures relative to the nerves for reference.

1. Medical System Overview

As shown in FIG. 6, the neurography system 10 included in medical system12 includes four basic components: MRI system 14, processing system 16,input system 18, and output/display system 20. In the preferredarrangement, the MRI system 14 is a conventional MRI system modified foruse in collecting image data of a patient P under examination. Theprocessing system 16 responds to operator inputs applied via inputsystem 18 to control MRI system 14 and process its output to display theresultant neurograms at system 20. As will be described in greaterdetail below, system 16 employs a variety of different imagingprotocols, alone or in combination, to ensure that the images producedare of a quality heretofore unachieved.

The medical system 12 includes a number of components that supplementthe imaging information produced by system 10 and/or use thatinformation for a variety of purposes. For example, an auxiliary datacollection system 22 may be included to collect image information aboutnon-neural structures, such as blood vessels and bone, in the imagedregion of patient P. This information can then be used to suppressand/or enhance the appearance of those structures in the neurogramsproduced by system 10.

A diagnostic system 24, included in system 12, may be used to analyzethe images produced by system 10. Given the high resolution, detail, andaccuracy of neurograms produced by system 10, system 24 can beprogrammed to analyze neural pathway information to detectdiscontinuities associated with, for example, neural compressions,injuries, and tumors. System 24 provides outputs indicative of thelocation of discontinuities and may, by consultation with a database ofimage information associated with clinically assessed abnormalities,provide an indication of the nature and magnitude of an imageddiscontinuity. These outputs can be used for diagnosis, or applied asfeedback to system 10 to refine a region of interest (ROI) underexamination in patient P.

Medical system 12 may also include a therapeutic system 26 and surgicalsystem 28. Systems 26 and 28 employ information about the patient'sneural structure from system 10 to assist in the proper administrationof a desired therapeutic or surgical operation. For example, theinformation may be used to guide a robotic stylus to a damaged neuralsite for treatment or to allow an operation on non-neural structure tobe performed without damage to the patient's peripheral nerves. Thesystems 26 and 28 may operate independent of physician control or maysimply provide the physician with real-time feedback concerning therelationship between an operation being performed and the patient'sneural structures.

A training and development system 30 is included in the medical system12 for a variety of different purposes. For example, the training system30 may be used to demonstrate the anatomy of various neural structures,along with their positional relationship to non-neural patientstructures. This information has great educational value given theextremely limited ability of prior an techniques, including directexamination, to provide detailed anatomical information. Training system30 may also be designed to analyze the effectiveness of neurographysystem 10 and provide feedback used to control the pulse sequences andother operational parameters of system 10.

As one final component, medical system 12 may include a host processingsystem 32 in addition to, or in place of, separate processing systems inthe other components of system 12. Although not separately shown in FIG.6, system 32 includes a central processing unit (CPU) coupled to theremainder of system 12 by input/output circuits. Memory is provided tostore software instructions, used to control the operation of the CPUand, hence, the various components of system 12, and to store image andother data collected by system 12. The use of a separate host processingsystem 32 is particularly desirable where various components of system12 are to be operated in interactive fashion pursuant to a single set ofsoftware instructions.

2. The Neurography System

Turning now to a more detailed discussion of neurography system 10, byway of introduction, some of the more important operational features ofsystem 10 are loosely depicted in the chart of FIG. 7. As will bedescribed in greater detail below, system 10 may be constructed toemploy one or more of these features to enhance the imaging ability ofconventional MRI sufficiently to provide diagnostically andtherapeutically useful information.

As shown, the operation of system 10 can be broken down into the broadsteps of data collection 34, image processing and analysis 36, imagedisplay 38, and control 40. The data collection process 34 involves, forexample, spin-echo imaging 42, which may be supplemented by one or moreof the following imaging protocols: fat suppression 44, diffusionweighting 46, and "long T2" processing 48, and other protocols includingmagnetization transfer. Each of these protocols has been found toenhance the quality of images of peripheral nerve sufficiently toprovide heretofore unavailable MRI neurograms.

The data collected by process 34 is subjected to image processing andanalysis 36, involving two-dimensional and:three-dimensional imagegeneration 50. Image generation 50 may be further enhanced bymiscellaneous suppression features 52, responsible for reducing theinfluence of, for example, blood vessels and patient motion, on theimages produced. An image subtraction feature 54 may also be employed toremove all non-neural components from the images.

a. Neurography System Construction

Having briefly summarized the operational aspects of neurography system10, its construction and operation will now be considered in greaterdetail. In one embodiment, MRI system 14 includes an imager I of thetype sold by GE Medical Systems, under the trademark SIGNA (softwarerelease 5.2).

In that regard, as shown in FIG. 8, the region R of the patient to beimaged is placed within the bore B of the MRI system imager I. As willbe described in greater detail below, the position of region R relativeto the imager may be stabilized by a splint 58. Splint 58 limits motionartifact, provides fiducial markers in a secondary frame of reference,and reduces the system's susceptibility to boundary effects thatotherwise might degrade fat suppression near the boundary between skinand air.

MRI system 14 includes polarizing field coils 60 and 61 responsible forexposing region R to the desired polarizing field. The polarizing fieldhas a strength of, for example, 1.5 Tesla and is oriented along az-axis.

A tuned rf excitation coil 62 is also positioned within bore B over theregion R under investigation. Coil 62 is provided with a pulsed rfinput, in a manner described below, to generate the field responsiblefor excitation of nuclear spins in region R. Coil 62 is also responsiblefor detecting the rf return, or echo, fields generated by the spins,although separate transmit and receive coils may alternatively be used.

The excitation coil 62 may be, for example, a solenoid or surface coil,configured and dimensioned to fit closely over the region R to be imaged(e.g., the patient's arm, leg, shoulder, chest, pelvis, head, neck orback). In a preferred arrangement, however, a phased array coil systemis employed to increase the signal-to-noise ratio of the returns,thereby providing an improvement in the spatial resolution of system 14and allowing information to be retrieved from signals that wouldotherwise have been too weak to form useful images. For example, whereperipheral nerve having a thickness on the order of 1-2 mm is to besharply resolved, each array includes, for example, 4-6 individualcoils, arranged in transverse and longitudinal pairs or linear pairedarrays.

Three pairs of gradient coils 64 and 66 are also positioned within thebore B of the imager. These coils superimpose a locational gradient ofroughly one Gauss per centimeter upon the polarizing field over thesample region R along each of the x, y, and z-axes. For the sake ofsimplicity, however, only the z-gradient coils 64 and 66 are shown inFIG. 8.

In the preferred arrangement, the same coil pairs 64 and 66 are used toproduce diffusional gradients along the desired axes, as well as therequisite locational gradients. Alternatively, one or more separatediffusional gradient coil pairs 68 and 70 may be provided within theimager bore B. If the separate coil pair 68 and 70 is mounted on amovable track, substantially any desired diffusional gradientorientation can be achieved. The diffusional gradient is relativelystrong compared to the locational gradients, e.g, ranging up to 10Gauss/centimeter or higher.

A computer 72 and front-end circuit 74 form the processing system 16,input system 18, and output/display system 20 of neurography system 10shown in FIG. 6. Computer 72 and circuit 74 cooperatively control andsynchronize the operation of MRI system 14, as well as process anddisplay the acquired data.

The computer 72 is, for example, an IBM-compatible personal computerincluding a 486 processor, VGA monitor, and keyboard. An interface bus76, included in circuit 74, couples computer 72 to the other componentsof circuit 74.

A gradient pulse generator 78 included in circuit 74 produces generallyrectangular output pulses used to establish the desired gradients in thepolarizing field. The output of generator 78 is applied to x-, y, andz-axis gradient field amplifiers 80, although only the z-axis amplifier80 is shown in FIG. 8. As will be appreciated, if separate coils 68 and70 are employed to establish the diffusional gradients, the output ofgenerator 78 must be applied to those coils via separate amplifiers 82.

Circuit 74 also includes an rf pulse generator 84, which produces rfsignal pulses used in the establishment of the excitation field. In thepreferred arrangement, the pulse generator produces an rf outputsuitable for use in proton MRI, although frequencies specific to otherMRI susceptible nuclei, such as, ¹⁹ fluorine, ¹³ carbon, ³¹ phosphorus,deuterium, or ²³ sodium, may be used. The output of generator 84 isamplified by a high-power rf amplifier 86 before being selectivelyapplied to the excitation coil 62 by a duplexer 88. The duplexer 88 isalso controlled to selectively steer the low level MR returns receivedby the excitation coil 62 to a preamplifier 90.

A mixer 92 transforms the high frequency output of preamplifier 90 to alow frequency signal by mixing the amplified MR returns with signalsfrom a digitally controlled rf oscillator 94, which also provides inputsto generator 84. The analog output of mixer 92 is input to a low passfilter 96 before finally being converted to a digital form by ananalog-to-digital converter 98. The computer 72 processes the resultantdigital inputs, which represent the response of the spins to the appliedfields, to generate the desired neurograms.

b. Neurography System Operation

Having reviewed the basic construction of the neurography system 10, itsoperation to generate the desired two- or three-dimensional neurogramswill now be considered. To that end, FIGS. 9 and 10 depict the generalsequence of steps performed by system 10 in the production ofneurograms. These neurograms exhibit a high nerve conspicuity, which forthe purpose of the ensuing discussion will be understood to refer to thecontrast (in, for example, intensity or color) between the nerve and theimage background. The methods described below may be used to produceneurographic images of substantially any region of the body, includingthe brain, for example, central nervous system (CNS) neurograms.

As indicated at block 100, the operation of the system is firstinitialized to establish certain parameters of the system's operation.In that regard, the operator may input desired parameters via computer72 in response to queries generated at start up. Because most aspects ofthe system's operation are controlled by software resident in the memoryof computer 72, default initialization parameters may also be accessed.

Although the particular parameters to be initialized may vary at theuser's discretion, examples include the type of images to be generated(i.e., two-dimensional cross sections or three-dimensional projections),field of view (FOV), thickness of each slice imaged, pulse repetitionrate (TR), number of phase encoding steps, the existence of a known axisof diffusional anisotropy, and the strengths and orientations of thediffusional gradients to be used. By way of example, the operator mayselect a two-dimensional image, a FOV of four cm by four cm, a TR of 1.5seconds, and 256 phase encoding steps. A discussion of anisotropic axisidentification is provided below.

Once initialization has been completed, a series of steps, correspondingto the data collection process 34 discussed in connection with FIG. 7,are performed. This process generally involves the control of pulsesequences used in connection with front end circuit 74. As will bedescribed in greater detail below, different sets of pulse sequences andcombinations of pulse sequences have been devised to unambiguouslydistinguish small peripheral nerves from neighboring structures ofsimilar shape and location, including the combination of certainexisting sequences into new groupings for use in new situations and thedesign of new sequences that incorporate optimized features for thepurpose of neurographic imaging. For illustrative purposes, a graphicillustration of one example of a suitable pulse sequence is provided inFIGS. 11A through 11F.

i. Fat Suppression

As indicated in block 102 of FIG. 9, a first, optional, step performedin the image generation process is fat suppression. Although fatrepresents a known source of interference in MRI images of bone andtissue, it was not previously recognized as an impediment to effectiveneural imaging due to the broader perception that neural MR signals wereinadequate for imaging regardless of background composition. The valueof fat suppression was discovered during the development of the presentinvention by the fortuitous use of a main field magnet designed forspectroscopy as part of an imaging system.

In that regard, in MR spectroscopy, a relatively strong magnetic fieldis employed to increase the separation in frequency between signalsarising from different chemical species of the same nucleus, therebyallowing these components to be more easily distinguished. MRI also usesa frequency distribution (created by applying a field gradient) over asample to locate spins and create an image. The signals from fat andwater are at slightly different frequencies and therefore appear shiftedrelative to each other in an image.

The fat/water shift is relatively small when a low field, clinical MRIsystem is used. Fortuitously, a much stronger spectroscopic field magnetwas used during initial efforts at imaging nerve, introducing a muchgreater displacement of fat in the image produced. With the highintensity fat signal shifted away from the nerve, an enhancement of thenerve's conspicuity was observed. The recognition of this enhancementled to the realization that effective neural imaging could, in fact, beachieved through the inclusion of fat suppression in system 14.

Fat suppression apparently enhances the use of conventional MRI systemsfor neurography in several ways. First, the removal of extraneouscomponents reduces the number of imaged structures to be distinguished.Second, in a fat suppressed image a peripheral nerve exhibits arelatively high intensity and will stand out sharply against the lowintensity space left behind by the suppressed fat. As will be describedin greater detail below, fat suppression also synergistically increasesthe apparent magnitude of diffusion anisotropy and magnetizationtransfer effect.

One suitable fat suppression technique involves the use of a chemicalshift selective (CHESS) pulse sequence, described in detail, forexample, in Haase et al. NMR Chemical Shift Selective Imaging, 30 PHYS.MED. BIOL. 341-344 (1985).

As shown in FIG. 11A, CHESS involves the application of a sequence ofnarrow band rf pulses A, B and C to the excitation coil 62 toselectively excite the nuclear spins of fat molecules within the regionR of the patient being imaged. By way of example, three millisecondGaussian pulses having a minus three dB bandwidth of 600 Hertz may beemployed. A sequence of gradient pulses a, b, and c is then applied tothe three sets of gradient coils 64 and 66 to dephase the excited spins,thereby minimizing the contribution of the fat signals to the finalimage. The gradient pulses a, b, and c applied to the orthogonalgradient coil pairs produce, for example, gradients of five Gauss percentimeter for three milliseconds along the x, y, and z-axis,respectively.

FIG. 12 illustrates the effect of fat suppression on neurograms producedwith the MRI system 14. The image provided in FIG. 12 is of the forearmof a rabbit and corresponds to the images of FIGS. 1-5 described above.The darker portions of the image represent greater image intensity. Asshown in FIG. 12, the ulnar nerve UN and median nerve MN are readilyidentified.

As an alternative to the use of CHESS for fat suppression, the desiredsuppression may be effected by selective water stimulation. Othersuitable alternatives include the Dixon technique for fat suppressiondescribed in, for example, Dixon et al., Simple Proton SpectroscopicImaging, 153 RADIOLOGY 189-194 (1984) and also STIR (short tau inversionrecovery) described in Improved Fat Suppression in STIR MR Imaging:Selecting Inversion Time through Spectral Display, 178 RADIOLOGY 885-887(1991).

Although in the preferred embodiment fat suppression is combined withother techniques such as diffusional weighting and long T₂ processing,fat suppression by itself enhances conventional MRI processingsufficiently to generate clinically useful neurograms. Similarly, aswill be described in greater detail below, other techniques employed bysystem 10 can be used without fat suppression to generate suitableneurograms.

ii. Spin-Echo Sequence (Without Diffusional Weighting)

Having discussed the optional introductory portion of the illustrativepulse sequence depicted in FIG. 11, the next phase of the neurographysystem's operation will now be considered.

In that regard, an rf excitation pulse D, shown in FIG. 11A, is appliedto coil 62 to tilt the net magnetic moment of the spins by ninetydegrees relative to the polarizing field, into the transverse plane. Theresultant maximum transverse magnetization then decays to zero as thespins dephase. A second pulse E, having twice the intensity of pulse D,is applied to coil 62 after a delay of one-half the return or echo time(TE). This pulse rotates the spins a further 180 degrees and causes aspin-echo to form as the spins rephase. The spin echo has a maximumamplitude after a further delay of TE/2. A spin-echo signal F is, thus,generated in coil 62 at time TE in response to the combined influence ofexcitation pulse D and refocusing pulse E. These steps are depicted inblocks 104, 106, and 108 of FIG. 9.

At the same time, the imaging gradients are produced by the orthogonalcoil pairs 64 and 66 to encode the echo signal F in the usual manner,allowing an MR image to be constructed, as indicated in block 110. Withthe sample oriented along the z-axis, the "slice select" pulses d, d',and e shown in FIG. 11C are applied to the z-axis coil pair 64 and 66,to excite and refocus the z-axis slice of interest. The "readoutgradient" pulses f and f, shown in FIG. 11D, are applied to, forexample, the x-axis coil pair 64-66 to achieve the desired output thatis to be Fourier transformed. The "phase encoding" pulses g and g',shown in FIG. 11E, are applied to the y-axis coil pair 64 and 66, tocontrol the number of echoes (e.g., 256) to be received. The sequencemay be used to generate images from contiguous slices or regions of thepatient.

As will be appreciated, if the operator indicates (at block 100) thatdiffusional weighting is not required for the generation of a particularimage by neurography system 10, the pulses shown in FIGS. 11A-11E definesubstantially the entire spin-echo sequence. Even if diffusionalweighting is to be employed, in the preferred embodiment an initialimage is generated using only fat suppression for enhancement and, as aresult, diffusional weighting is not used during the first performanceof the spin-echo sequence (blocks 104-110) for a particular slice.

Although spin-echo imaging is employed in the preceding embodiment ofneurography system 10, other techniques can be employed. Suitablealternative techniques include, for example, stimulated echo imaging andgradient-recalled echo imaging, e.g., echo planar imaging (EPI). Suchalternative techniques are described in Parikh, MAGNETIC RESONANCEIMAGING TECHNIQUES (1992).

iii. Echo Processing

In the imaging sequence depicted in FIG. 11, a series of echo signals Fare acquired to create a two-dimensional image. For example, at block112 in FIG. 9, 256 echoes with 256 different phase encoding gradientamplitudes are used to construct a 256-by-256 pixel image. The data setis then enlarged at block 114 by zero filling to produce a 1024-by-1024matrix of data. As a result, the apparent resolution of the final imageis increased, making the image clearer.

Next, the enlarged data set is processed using a 2D Gaussian filter atblock 116. The filter smoothes the image by attenuating the highfrequency components in the image and, thus, clarifies the delineationof small details without altering the relative average pixel intensitiesover a region of interest. At block 118, the two-dimensional matrix ofdata then undergoes a two-dimensional Fourier transform, which yields animage to be stored. If desired, the image may also be displayed on thecomputer monitor, although in the preferred arrangement this image isbut one component used in a more extensive analysis performed togenerate a select, enhanced image.

Once an initial image has been generated, the analysis of the image isinitiated, as shown in FIG. 10. At block 122, one or more regions ofinterest (ROI) within the image can be identified. Each ROI may be asingle pixel or voxel, or a larger region. ROI selection can beperformed manually using, for example, a keyboard or mouse to move acursor over the ROI on the displayed image. Alternatively, ROI selectionmay be accomplished automatically via a sequential selection of allpixels or via an external input regarding a particular region from, forexample, diagnostic system 24.

Next, the average image or pixel intensity within each ROI is computedat block 124. This average image intensity S can be represented by thefollowing expression:

    S=A.sub.0 [exp (-TE/T.sub.2)][exp (-bD)]                   (1)

where A₀ is the absolute signal intensity for a particular pixel and bis the gradient factor, determined in accordance with the expression:

    b=γ.sup.2 (G.sub.i.sup.2)(δ.sup.2)(Δ-δ/3)(2)

where γ is the gyromagnetic ratio, G_(i) is the polarizing fieldstrength, δ is the length of a diffusional weighting gradient pulse, andΔ is the interval between diffusional weighting gradient pulses. As willbe appreciated, in the first iteration before diffusional weighting isemployed, the final term of equation (1) is, thus, unity.

To make use of the expressions in equations (1) and (2), the precedingdata acquisition process is repeated for different values of echo timeTE. On the other hand, if diffusional weighting is employed, asdescribed in greater detail below, the data acquisition process isrepeated for different gradient strengths (controlled by adjustinggradient magnitude and/or duration) or gradient orientations. Forexample, TEs of 30, 60, 90, and 110 milliseconds, or gradient magnitudesof 0, 3, 5, and 7 Gauss/centimeter, may be employed. The image intensityS for a particular pixel of these multiple images of the same transverseslice for particular values of TE (or b, if diffusional weighting isemployed) is available and a linear regression analysis of thelogarithmic relationship is performed at block 126.

Finally, the value of the apparent T₂ relaxation time (or the apparentdiffusion coefficient D, if diffusional weighting is employed) iscomputed for a particular ROI at block 128. These computations providequantitative assessments of the various ROI in the image that are usefulin subsequent image processing by other components of the medical system12.

iv. Gradient Orientation for Diffusion Weighting

In the preferred arrangement, after the initial fat suppressed image hasbeen collected and its ROI characterized, a diffusional weightinganalysis is initiated to further enhance the neurograms generated byevaluating the diffusional anisotropy exhibited by nerve and othertissue. The first aspect of this analysis is the selection of thediffusional gradients to be used.

By way of introduction, in one currently preferred embodiment, theanalysis involves the application of pulsed magnetic field gradients tothe polarizing field in two or more directions to produce images inwhich the peripheral nerve is enhanced or suppressed, depending upon the"diffusion weighting" resulting from the particular pulsed gradient axischosen. Discrimination of water diffusion anisotropy is then achieved bysubtracting the suppressed image from the enhanced image, in the mannerdescribed in greater detail below, producing an image depicting only theperipheral nerve.

Most preferably, the magnetic field gradients are applied in mutuallysubstantially orthogonal directions. For example, with gradientsapproximately perpendicular and parallel to the axis of the peripheralnerve at the particular point being imaged, the parallel gradient imagecan be subtracted from the perpendicular gradient image to produce thedesired "nerve only" image.

As will be appreciated, if the axis of the nerve is generally known tothe operator and its relationship to the referential frame of the MRIsystem 14 has been indicated at initialization block 100, the directionof the desired orthogonal diffusional weighting gradients can be readilydetermined. On the other hand, if the axis of the peripheral nerve isnot known, or if many;nerves having different axes are being imaged, theneurography system 10 must employ a system of gradient orientationssuitable for imaging nerve having substantially any axial alignment. Forexample, as will be described in greater detail below, a fullthree-dimensional vector analysis can be used to characterize thediffusion coefficient and provide a nerve image by construction basedupon a fixed arrangement of diffusion weighting gradients.

In anatomical regions, such as the upper arm or wrist, it is alsopossible to achieve adequate enhanced isolation of the nerve image byapplying only a single diffusion gradient perpendicular to the axis ofthe nerve at the site of interest. As a result, no subtraction need becarried out to produce the neurogram. The fat suppressed, orthogonallydiffusion weighted image can either be processed directly, or it can besubject to threshold processing to remove signals of lower intensityassociated with non-neural tissue, or nerves with different axes anddirections of travel at the imaging location.

As will be appreciated, for quicker and more efficient data collectionand processing, the establishment of diffusion gradients in thepolarizing field should be responsive to the particular one of theforegoing scenarios that applies to the imaging problem at hand.Depending upon the inputs provided at block 100, the system may havebeen advised that (a) only one gradient of known orientation isrequired, (b) two orthogonal gradients of known orientation arerequired, or (c) two or more gradients of unknown orientation arerequired.

As indicated in block 130 of FIG. 10, upon completion of the analysis ofan image, the system considers whether all of the desired diffusionalgradients have been applied to the polarizing field during subsequentdata acquisition by, for example, spin-echo processing, or fastspin-echo processing. Because no diffusional gradient was used in theinitial fat suppression processing, the answer is initially NO andoperation proceeds to block 132.

There, the computer determines whether the operator initially indicatedthat the axis of diffusional anisotropy is known. If the axis is known,a perpendicular diffusional gradient is employed, as indicated at block134. Then, as indicated at block 136, a diffusion-weighted spin-echosequence is performed (modified by the inclusion of the diffusionalgradient in the manner described in greater detail below) and imagegenerated, pursuant to blocks 102-122, before quantification of theimage data occurs at blocks 124-128 to compute D or T₂. If the operatorindicated at initialization that orthogonal diffusion gradients arerequired for the particular imaging problem at hand, this process isthen repeated at blocks 138 and 140 for a parallel diffusional gradient.

If the inquiry performed at block 132 determines that the axis ofdiffusional anisotropy is unknown, operation proceeds to block 142.There an initial diffusional gradient is arbitrarily selected, to befollowed by a sequence of alternative gradients selected for use by theoperator when the anisotropic axis is unknown.

At block 144, using the initial diffusional gradient, a spin-echosequence is performed (modified by the inclusion of the diffusionalgradient in the manner described in greater detail below) and imagegenerated, pursuant to blocks 102-122, before quantification of theimaged data occurs at blocks 124-128. Then, at block 146, a test isperformed to determine whether the desired number of differentdiffusional gradients (e.g., three gradients, along the x-, y-, andz-axes) have been used. If not, the next diffusional gradient isselected at block 148 and the spin-echo sequence, imaging and processingoperations are performed, as indicated at block 144. This process isthen repeated until the desired number of alternative diffusionalgradients have been employed.

As will be appreciated, additional gradient coils may be provided wheregradients are desired along axes other than those provided by thelocational gradient coils. To that end, diffusional gradient coils maybe mounted on a magnetically compatible, adjustable track within thebore of the imager to allow gradients to be repositioned and appliedover a substantially continuous range of orientations. Similarly, theregion to be imaged may be movably supported relative to a fixed set ofgradient coils to introduce the desired variability in gradientdirection. As another option, a plurality of different gradient coilsmay be employed and activated in various combinations to effect thedesired gradient variations. Alternatively, the results obtained from alimited number of gradient directions can be processed using a vectoranalysis to estimate the results obtainable with a gradients other thanthose directly available, as described in greater detail below.

v. Spin-Echo Sequence For Diffusional Weighting

As noted briefly above, for each of the different diffusional gradientsemployed, the spin-echo sequence is repeated, followed by the generationof image data and the processing of that data to, for example, quantifythe relaxation time T₂ or diffusion coefficient D. In the preferredarrangement, the use of diffusion gradients influences a number ofaspects of the spin-echo sequence.

As shown in FIG. 11F, two pulses h and h', applied to the desired pairof gradient coils are used to establish a particular diffusionalgradient in the polarizing field. For an echo time (TE) of 50milliseconds, the duration (δ) of each pulse is, for example, 10milliseconds and their separation (Δ) is 20 milliseconds. In thepresence of the diffusional gradient, the echo signal F, and thereforethe pixel or voxel intensity in the image ultimately produced, is madesensitive to the spatial diffusion of water molecules in the imagedregion R.

In that regard, as indicated above, with the diffusional gradientoriented substantially perpendicular to the diffusionally anisotropicnerve, the nerve image is enhanced and generally exhibits the highestintensity of various features imaged. This phenomena is depicted in FIG.13A, which is an image of the forearm of a rabbit, corresponding to thediagram provided in FIG. 1. The ulnar nerve UN and median nerve MN areboth relatively dark (high intensity) and are easily seen.Alternatively, with the diffusional gradient oriented substantiallyparallel to the diffusionally anisotropic nerve, the nerve image issuppressed and generally exhibits a lower intensity than other featuresimaged, as illustrated in FIG. 13B. These images can be combined, via asubtraction process described in greater detail below, to produce animage of the nerve isolated from all other structure.

To reduce the effect of cross-terms between the imaging gradients andthe diffusion weighting gradients, the spin-echo sequence illustrated inFIG. 11 is a modified version of conventional sequences. Moreparticularly, the readout gradient rephasing pulse f shown in FIG. 11D,is placed directly before the acquisition of echo F in FIG. 11A, insteadof after the slice-selective excitation pulse d, shown in FIG. 11C.However, a consequence of this change was the appearance of artifacts inthe non-diffusion-weighted images due to an unwanted echo, presumablyformed from imperfections in the slice-selection pulses d, d', and e,shown in FIG. 11C. To overcome this problem, a second modification ofthe pulse sequence was made. Specifically, the phase-encoding gradientwas split into two sections g and g', and two or four transients(depending upon S/N) were acquired with phase cycling. As a result, theremaining cross terms contribute less than three percent to thediffusion weighting factor.

Although fat suppression is not required to take advantage of the imageenhancements available through diffusional weighting gradients, in thepreferred arrangement, the fat suppression sequence shown in FIGS. 11Aand 11B is employed prior to the initiation of the diffusion-weightedspin-echo sequence. As will be described in greater detail below, thecombination of these techniques generally provides an image quality thatexceeds that available from either technique individually.

The echo F produced using the diffusion weighted pulse echo sequence isprocessed in the manner described above in connection with blocks 112through 128 of FIGS. 9 and 10. With diffusion weighting, the computationof the diffusional coefficient D at block 128 is preferably based uponthe analysis of data collected for different gradient magnitudes. Forexample, the computation may be based upon gradients of 0, 3, 5, and 7Gauss/centimeter, resulting in the production of image data asrepresented in FIGS. 14A through 14D, respectively. While fat, bone,marrow, skin and vessels are generally absent even at the lowergradients, muscle and ligaments drop out at the higher gradients. Assuggested previously, the increasingly stronger gradients may beachieved by increasing gradient duration, rather than magnitude.Alternatively, the iterative data collection process may be performedusing different gradient directions.

vi. Image Selection/Production

Once computer 72 determines, at block 130, that images have beencollected for all of the desired diffusional gradients, operationproceeds to block 150. If the axis of anisotropy is unknown, the variousdiffusional coefficients D computed for each ROI using differentgradient orientations are compared at block 150 to identify the maximumand minimum values. These coefficients provide a measure, associatedwith each pixel or voxel, of the magnitude of diffusional anisotropy atthat point, while the anisotropic direction is indicated by the gradientorientation.

(a) Subtraction Neurography

In the preferred arrangement, the images associated with the maximum andminimum values of the diffusional coefficients for a particular ROI arethen used in a subtraction process, as indicated at block 152. The imageassociated with the larger coefficient is produced by a gradient that ismore nearly perpendicular to the neural axis, enhancing the nerve image,while the image associated with the smaller coefficient is produced by agradient that is more nearly parallel to the axis, selectivelydestroying the nerve signal. When these two penultimate images are thenmathematically (or photographically or optically) subtracted from oneanother, a subtraction neurogram is produced.

By way of illustration, FIG. 15A is an image produced without diffusionweighting. FIGS. 15B and 15C then illustrate images produced usingparallel and perpendicular gradients, respectively. Finally, thesubtraction neurogram produced when the image of FIG. 15C is subtractedfrom that of FIG. 15B is shown in FIG. 15D.

This "ideal" neurogram is somewhat analogous to a subtraction angiogram(an image showing only blood vessels), but sharply highlights a nerverather than a vessel. Such an image is particularly useful forconfirming the identification of nerves in a given imaging plane orspace as well as for locating nerve injuries and nerve compressions.Despite the well known existence of angiograms showing the entirevascular pattern in an anatomic region, and despite the existence of MRItechniques that could have been applied to the problem of neural imagingtechniques, and despite the great need for the visualization of nerves,particularly, in isolation, there has not previously been any way ofcreating such neurograms.

Although image subtraction is employed in the preferred arrangement, itis not necessary. For example, in some applications of known anisotropy,subtraction is unnecessary and can be foregone in favor of a thresholdanalysis. Also, the subtraction process can be further supplemented, ifdesired. For example, the output of the subtraction process can bedivided by the signal information from a fat suppressed, T₂ -weightedspin echo sequence (e.g. using the aforementioned CHESS technique).

One potential problem to be addressed by the use of the subtractionprocess is image registration. As will be appreciated, provided thatnon-neural tissue is identically located in both images subjected to thesubtraction process, the non-neural component will cancel out of theresultant image. On the other hand, if some shift or other discrepancyin the apparent position of non-neural tissue is introduced into animage due, for example, to movement of the subject, cancellation may notoccur and the nerve may actually be more difficult to identify in theresultant image.

In one embodiment, acceptable image registration is evaluated prior toinitiation of the subtraction process. More particularly, theintensities of pixels in one image are compared to the intensities ofcorresponding pixels in the second image. Pixels of neural tissue aredisqualified on the basis of their high diffusional anisotropy, assessedvia their diffusion coefficients. Unless the intensities of theremaining, non-neural pixels fall within a certain range of each other,indicating acceptable image registration, subtraction will be inhibited.

(b) Vector Processing and Three-Dimensional Image Generation

Up to this point, the output produced is generally in the form of asingle two-dimensional image, or a series of two-dimensional images thatcan be related to form a three-dimensional image. In a simple form ofthree-dimensional image generation, described in greater detail below,the high S/N ratio of the two-dimensional neurograms produced by system14 readily allows the imaged nerve cross-sections to be identified andthen linked together to form a three-dimensional projection of theneural structure.

As will be appreciated, however, depending upon the neural patterninvolved and the spatial resolution required, this simplified approachmay introduce undesired discontinuities into the three-dimensionalprojection. A more sophisticated processing scheme employs informationabout the anisotropic direction of the nerve in each two-dimensionalimage to further enhance the accuracy of three-dimensional imageprojections. The availability of information regarding anisotropicdirection is also useful in establishing the optimal directions for thegradients used in the diffusional weighting analysis described above toproduce a two-dimensional image.

In that regard, the anisotropic axis of the peripheral nerve beingimaged is sometimes known to the operator, allowing the operator toinput the directional information at block 100 and select the bestdiffusional gradients for imaging. More commonly, however, nerves andCNS neural tracts follow relatively complex paths and the direction inwhich the diffusion anisotropy coefficient of the nerve or tract isgreatest gradually shifts from one plane or axis to another as the nerveor tract curves or turns. As a result, one or two arbitrarily oriented,standard gradients may be inadequate to provide the desired images.

Changes in neural direction can be monitored by moving the patientrelative to a fixed set of gradient coils or employing movablediffusional gradient coils mounted, on a track with a non-magnetic drivesystem, within the bore of the imager to adjustably control theorientation of the diffusional gradients applied to the region ofinterest. By monitoring changes in the ratio of D_(pl) /D_(pr) obtainedfor a given pixel using alternative gradient alignments, or forsequential pixels using the same gradient alignments, changes in neuraldirection can be estimated and suitable gradient directions selected.Alternatively, gradient coils oriented in three planes can besimultaneously activated in various combinations to achieve the effectof an infinite variety of differently oriented gradients.

One advantage of attempting to track changes in neural direction is thatparallel and perpendicular gradient information can then be collectedand used to produce a subtraction neurogram of the type described above.If, however, the optimal gradient directions for a given pixel aredetermined using feedback from images generated with repetitivelyadjusted gradients, processing speed may be significantly impaired.

In many cases the well known anatomy of a nerve will permit the use of aparticular axis orientation in advance. Initial imaging information willprovide a description of the gross course of the nerve. A subsequent"informed" approximation can optimize the orientation in each slice.This can be useful to insure excellent homogeneity of nerve imageintensity or to measure the coefficient of anisotropy along the courseof the nerve.

As a preferred alternative, requiring less mechanical complexity andfaster processing speed, a technique has been developed for observingdiffusional anisotropy, independent of its degree of alignment with anyindividual gradient axes. This process involves the combination ofinformation from anisotropy measurements obtained along three standardorthogonal axes or using information from multiple fixed axes. Forexample, in the preferred embodiment, a vector analysis is used toproduce interpolated images and directional information from the threeorthogonal diffusion-weighted images described above.

In that regard, image information is collected from, for example, four"multi-slice" sets using a zero diffusion gradient B₀ and diffusiongradients B_(x), B_(y), B_(z) in the x-, y-, and z-orthogonaldirections, respectively. For each pixel in the image to be produced,information concerning the corresponding pixels in the four diffusiongradients images is combined to produce a diffusion vector,representative of water molecule movement along the nerve fiber ineither direction. This vector has a magnitude representative of theimage intensity of the pixel and a direction representative of an"effective" diffusion gradient associated with the pixel.

More particularly, the image intensity S_(n) of a given pixel in the newimage is calculated using the following vector equation:

    S.sub.n =vector length=[(S.sub.x.sup.2 +S.sub.y.sup.2 +S.sub.z.sup.2)S.sub.o.sup.2 ].sup.1/2                    (3)

where S_(x), S_(y), and S_(z) are the image intensities of thecorresponding pixels in the images produced by the B_(x), B_(y), andB_(z) gradients. S₀ is the image intensity of the corresponding pixel inthe image produced by the B₀ gradient and is included in equation (3) tonormalize the resultant image intensity S_(n). The direction of theeffective gradient associated with this pixel image includes componentsθ_(xy), θ_(xz), and θ_(yz), computed in the following manner:

    θ.sub.xy =diffusion vector angle between B.sub.x and B.sub.y =arc tan (S.sub.y /S.sub.x)                                        (4)

    θ.sub.xz =diffusion vector angle between B.sub.x and B.sub.z =arc tan (S.sub.x /S.sub.z)                                        (5)

    θ.sub.yz =diffusion vector angle between B.sub.y and B.sub.z =arc tan (S.sub.y /S.sub.z)                                        (6)

The parameters computed in equations (3), (4), (5), and (6) can be usedto generate images in a variety of different ways. For example, theintensities of the pixels can be displayed as a "vector length" image.An illustration of a vector length CNS image, in which the intensity ofthe image is proportional to the magnitude of S_(n) is shown in FIG. 16.

The image of FIG. 16 is a brain scan of a monkey (macaca fascicularis)weighing 2-2.5 kg, performed using diffusion imaging (spin-echo) on aGeneral Electric CSI II imager/spectrometer (2 Tesla, equipped withactively shielded gradients). The acquisition parameters were: TR=1000ms, TE=80 ms, diffusion gradients=5 Gauss/cm, diffusion gradientduration=20 ms, diffusion gradient separation=40 ms. Four slices ofthickness 4 mm were imaged. T₂ -weighted images were used toreproducibly select the diffusion images.

As an alternative to the use of vector length images, arctan images canbe employed. These images are obtained by establishing the intensity ofa pixel in direct proportion to the angular output of one of equations(4), (5), or (6). An illustration of an arctan image is provided in FIG.17. As shown in this example of a CNS neurogram, a select neural tractof interest can be effectively traced and made to stand out in isolationfrom other neural tracts.

When used to evaluate lesions in CNS images of the type shown in FIGS.16 and 17, vector length images will be more sensitive to waterdiffusion changes where all three orthogonal images change in the sameway, while the vector angle images will be sensitive to changes inanisotropy between two orthogonal directions. A CNS lesion caused byexperimental allergic encephalomyelitis induced by myelin basic proteinis demonstrated by its departure from the diffusional anisotropy, whichappears as vector length decreases and image intensity changesaccentuated in particular vector angle images.

Alternative forms of vector analysis can also be applied, for example,as described in Basser et al., Fiber Orientation Mapping in anAnisotropic Medium with NMR Diffusion Spectroscopy, SMRM BOOK OFABSTRACTS 1221 (1992). Similarly, tensor analyses employing tensors ofvarious ranks, as described in Basser et al., Diagonal and Off DiagonalComponents of the Self-Diffusion Tensor: Their Relation to an Estimationfrom the NMR Spin-Echo Signal, SMRM BOOK OF ABSTRACTS 1222 (1992), canbe used to treat, or transform the coordinates of, MR diffusionalanisotropy data. Suitable alternative processing techniques have beendeveloped for use in the evaluation of magnetic, thermal, and structuralanisotropy data.

Unlike prior art systems, because the non-neural components of theneurograms produced by system 14 exhibit a relatively low intensity, orindeed disappear entirely from the images, the computer 72 is readilyable to identify nerve locations in the anatomical structure and tocorrectly trace the course of the nerves between two-dimensional imageplanes or through a three-dimensional acquisition volume. For example,the location of nerves in a given image plane can be detected bycomparing pixel intensity to some threshold level. A three-dimensionalimage can then be formed by linking or projecting the results of thesetwo-dimensional analyses over the desired volume.

As an alternative, the vector information obtained above can be used totrack continuous serial changes in the direction of maximum anisotropyof a nerve or neural tract as the nerve or tract travels along itsnatural course. In that regard, the direction of maximum anisotropy foreach voxel associated with a nerve is determined and a voxel connectionroutine, of the type described in Saloner et al., Application of aConnected-Voxel Algorithm to MR Anglographic Data, 1 JOURNAL OF MAGNETICRESONANCE IMAGING 423-430 (1991), is then used to link up voxels ofmaximum anisotropy. The resultant plot of the nerve or neural tractprovides enhanced spatial resolution and less discontinuity from oneimage plane to the next.

As an alternative to the two-dimensional imaging sequences describedabove, it is also possible to carry out the signal acquisition using a"three dimensional" imaging sequence of the type described in Frahm etal., Rapid Three-Dimensional MR Imaging Using the FLASH Technique, 10JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY 363-368 (1986). The output ofthis sequence is then processed using a three-dimensional Fouriertransform to extract the returns from nuclei over the volume beingimaged. The resultant processing used to compute D for a given voxel andto generate, for example, a subtraction angiogram is substantially thesame as described above.

Regardless of the routine employed to project the neural structure inthree-dimensions, the system 10 may be further programmed to implementthe projection by referring to known characteristics of the structure.More particularly, once a given nerve has been identified in a giventwo-dimensional image, an "expert" system 10 is able to predict theoccurrence of certain branches and mergers in this structure, albeit atunknown locations. This information can then be used to test theplausibility of the projection being generated, refining it wherenecessary.

vii. Results of Combined Fat Suppression and Diffusion Weighting

As previously noted, both muscle and nerve exhibit diffusionalanisotropy. In view of the relatively low signal strength of neuralcomponents, diffusional analyses were not expected to provide clinicallyuseful neurograms. The combined use of fat suppression and diffusionalweighting has, however, been found to be extremely effective inproviding the desired nerve image enhancement.

By way of illustration, for a gradient strength of 7 G/cm and an echotime of 50 ms, an nerve image signal intensity (S_(n)) of 17 and amuscle image signal intensity (S_(m)) of 7 were calculated, based uponthe difference between signal intensities with pulsed gradients orientedperpendicular and parallel to the nerve. A nerve-to-muscle contrastparameter R of 2.43 was then computed as the ratio S_(n) /S_(m).Similarly, a comparison of the apparent diffusion coefficients fordiffusional gradients perpendicular (D_(pr)) and parallel (D_(pl)) tonerve and muscle are as follows:

    ______________________________________                                        Apparent Diffusion Coefficients (10.sup.-5 cm.sup.2 /sec)                                   Muscle                                                                              Nerve                                                     ______________________________________                                        D.sub.pr        1.17    0.65                                                  D.sub.pl        2.18    2.00                                                  D.sub.pl /D.sub.pr                                                                            1.9     3.1                                                   ______________________________________                                    

These results clearly illustrate that the neural components exhibit afar larger relative change in intensity than muscle components whensubjected to diffusion anisotropy analysis.

An unexpected and apparently synergistic benefit of fat suppression,when used in combination with diffusional weighting, is that an actualincrease in neural signal anisotropy is experienced, with theconspicuity of the neural component of the image increasing by roughly250 percent when the fat component is removed. The combined increase innerve conspicuity and reduction in fat interference significantlyenhances the effectiveness of neural imaging.

Although not entirely understood, there are several potentialexplanations for the synergistic relationship between fat suppressionand diffusional weighting. First, it appears that fat suppression mayincrease the apparent diffusional anisotropy of nerve, enhancing theutility of diffusional weighting gradients in the detection of neuraltissue. By way of illustration, an indicated in the following test data,obtained with the signal from fat and "short T₂ " water removed, theintensity of the remaining image signal was due largely toanisotropically diffusing water.

    ______________________________________                                                 CHESS Applied                                                                             CHESS Not Applied                                                 Gradient Direction                                                                        Gradient Direction                                       Nerve Imaged                                                                             pr     pl     Ratio pr    pl    Ratio                              ______________________________________                                        Ulnar Nerve                                                                              29     <8     >3.6  62    49    1.3                                Median Nerve                                                                             30     <8     >3.8  46    22    2.1                                Muscle     14      8      1.8  18    12    1.5                                ______________________________________                                    

The synergistic role of fat suppression can also be viewed as ademonstration of a magnetization transfer effect. More particularly, theirradiation of protons on e.g,. myelin lipids surrounding the nerve bythe saturation pulse of the fat suppression sequence may allow transferof the saturation pulse to water molecules in close association with thelipid, allowing for very efficient transfer. Subsequently, thesemolecules can exchange into the anisotropically diffusing mobile waterpool.

vii. Long TE/TR/T₂ Processing

As an alternative to the use of diffusional gradients described above,in some regions of interest, it is possible to achieve adequate enhancedisolation of the nerve image by use of a spin echo fat suppressiontechnique with a relatively long TE (echo time) or TR (repetition time)to achieve a T₂ -weighted image. In that regard, after fat suppression,the dominant component remaining in the echo F is returned from muscle.Because the T₂ of peripheral nerve has been measured by the inventors tobe roughly twice as long as the T₂ of muscles, the use of a relativelylong TE or TR in the spin echo sequence allows the muscular return to beremoved.

The basic operation of a neurography system 14 employing this featureremains the same as that shown in FIGS. 9 and 10 except that theinitialized value for TE is extended. In that regard, the operator maybe called upon to initially consider whether the desired imaging islikely (e.g., neural imaging in a patient's limbs) or unlikely (e.g.,CNS imaging) to be disrupted by the presence of muscle. If muscularinterference is likely, a relatively long TE of between 50 and 100milliseconds or even longer is initialized at block 100. The particularTE or TR selected depends upon the degree of T₂ weighting desired.Alternatively, the system 14 may be programmed to compare the imagingdata separately collected using long TE processing and diffusionalweighting to assess which provides the best results.

Illustrations of the results available with long TE imaging are providedin FIGS. 18A through 18D, for TE equal to 30, 40, 60, and 100milliseconds, respectively. In the image of the forearm of a rabbit,provided in FIG. 18D, produced with a field strength of 4.7 Tesla,nerves are brighter than any other structure in the image. The extent ofthe increased nerve conspicuity is on the order of ten-fold, renderingthe images clearly susceptible for use in constructing neurograms. Aswill be appreciated, lesser conspicuities on the order of 1.1 may alsobe useful.

The use of extended TE processing had previously been consideredunfeasible. In that regard, as described in Moseley et al., Anisotropyin Diffusion-Weighted MRI, 19 MAGNETIC RESONANCE IN MEDICINE 321, 325(1991), nerve was believed to exhibit a relatively short T₂ time.Surprisingly, however, measurements have been conducted indicating thatthe T₂ of muscle is approximately 27 milliseconds, while the T₂ ofperipheral nerve is approximately 55 milliseconds, providing a factor oftwo difference between the two types of tissue.

ix. Additional Enhancements for Neural Imaging

(a) Vessel Suppression

In addition to the fat suppression and muscle suppression techniquesdescribed above, vessel suppression may be employed to improve theneurographic selectivity of the images generated by system 14. Due tothe brightness of slowly moving blood in some otherwise usefulsequences, vessel suppression has particular value when used inconnection with long TE sequence neurograms.

A variety of alternative approaches can be employed to achieve thedesired blood vessel suppression. For example, in a first embodiment,the blood vessels are separately imaged to produce a flow-based MRangiogram, employing phase contrast or time-of-flight information. Theangiogram may be produced using the MRI system 14, under separateprogram instructions, or the auxiliary data collection system 22described below. The angiogram is then subtracted from the neural imageto provide a neurogram in which blood vessels content is completelysuppressed.

As previously noted in connection with the discussion of imagesubtraction, a registration problem occurs when the information to beremoved from one image is not identically represented with the sameintensity and location in the subtrahend image. With vessel imageinformation obtained using an angiography pulse-echo sequence (or othertechniques described below) and neural image information obtained usingthe neural pulse-echo sequence described above, some difference invessel intensity in the two images is to be expected. One way ofavoiding registration error in this situation is to normalize theangiogram to the corresponding neural image (i.e., equalize angiogramintensity based upon comparative measurements at a vessel pixelidentified on the angiogram).

A second technique used for vessel suppression is to employ a short TEsequence to produce a first image in which blood vessels are relativelybright and nerves are relatively dim. This image is then subtracted froma second image obtained using a long TE and exhibiting bright nerves anddim vessels.

A third blood vessel suppression technique involves the administrationof an intravenous "black blood" contrast agent to the vessels. The agentis preferably (but not necessarily) of the "blood pool" type includingdysprosium-DOTA poly lysine or iron oxide type contrast agent. The bloodvessels are thereby blacked out by the pharmaceutical agent so there isno need for a subtraction step to produce the desired vessel-suppressedneurogram.

Finally, carefully adjusted water suppression techniques can be used tolimit the contribution of the blood vessels and cerebro-spinal fluid(CSF) to the neural image generated by system 14. One such technique isfluid-attenuated, inversion recovery (FLAIR), described in, for example,Bydde et al., Comparison of FLAIR Pulse Sequences with Heavily T₂Weighted SE Sequences in MR Imaging of the Brain, 185 RADIOLOGY SUPP.151 (1992).

(b) Motion Suppression

Some of the image processing techniques described above, including, forexample, the use of diffusion weighting, may be adversely influenced bymotion of the region being imaged. To limit the introduction ofambiguous or erroneous content into the images produced (i.e., motionartifact), several different hardware and software features may beemployed by the neurography system 14.

As shown in FIG. 19, the acquisition of image information can beelectively carried out with the region of the patient under examinationimmobilized by a splint 156. The splint 156 includes a rigid base 158,made of plastic or some other non-ferro-magnetic material. Base 158 isincluded to provide a fixed frame of reference for the region underexamination and is designed to be optionally rigidly secured within thebore of the imager I. As will be described in greater detail below, oncethe neurography system 10 has imaged the region, the reference frameprovided by base 158 allows another system, like surgical system 28, tooperate within that reference frame in a known relationship to theimaged nerves. A non-rigid system, described below, employs fiduciarymarkers applied to the skin surface within the splint.

A rigid frame 160, made of plastic or some other non-ferromagneticmaterial, is attached to base 158 and provides structural support forthe splint 156. One or more fiduciary markers 162, e.g. water-filledbeads or linear marker strips, are provided on the frame 160 and/or base158 to allow the relationship of frame 160 and base 158 relative to theimaged region to be determined from the images generated. In thepreferred arrangement, each marker 162 extends the length of splint 156,so that it is visible in each cross sectional image generated. At leastone of the markers or strips 162 is aligned at a non-zero angle to thex, y, and z axes of the image plane, ensuring that its particularlocation in the image provides a positional reference to the splint.

A sleeve 164, made of a thin film plastic and filled with a conformablesubstance 166, such as water containing gel, silicone, foam, orcobalt-chloride doped water, is formed around the frame 160 and includesstraps 168 for use in attaching the splint to the patient. As shown, theframe 160 and sleeve 164 include a number of open regions 170, providingaccess to select areas of the region under examination by, for example,surgical system 28. Alternatively, the base 158 may be used with twosleeves. A first such sleeve provides complete and continuous skincontact for imaging, while a second sleeve secures the arm but generallyallows access by a surgical device.

A pump 172 is included to allow the fluid to be introduced into sleeve164 from a reservoir 174 under pressure, forcing the sleeve against thepatient's skin and immobilizing the region under examination. A releasevalve 176, allows the fluid within the sleeve 164 to return to thereservoir 174, relieving pressure within sleeve 164.

In addition to reducing motion artifacts, the splint 156 performsseveral other functions. First, as suggested above, the splint 156provides a reference frame that can be used by other components ofmedical system 12 to ensure that actions are taken in properrelationship to an imaged neural network. Second, the splint 156 may berequired to keep the region under examination immobilized for thesuccessful administration of therapy or performance of surgery by system12.

A third function of splint 156 is the reduction of edge effects thatmight otherwise be experienced using fat suppression. In that regard,the surface of a region under examination (i.e, the patient's skin)presents an abrupt transition in the nature of the material beingimaged. The field inhomogeneity caused by this tissue-to-air interfacecauses the fat signals in the patient's surface adipose tissue to spreadout and/or shift in frequency relative to deeper lying fat surroundingthe nerve. The desired effect of fat suppression is, however, tosuppress the signal from the underlying tissue adjacent the neuraltissue to be imaged. By employing thin flexible polyethylene or otherplastic for sleeve 164 and paramagnetically doped water for substance166, these edge effects introduced at the surface of the region underexamination are reduced on the order of 1.5 to 5 for nerve tissues.

Additionally, the splint can be specially designed to move a particularbody region during an imaging series to introduce serial steppedrepositionings of the limb. The movement can be controlled externally bya hydraulic system with fiber optic feedbacks to assess repositioning.In this fashion, it is possible to collect a series of images with thelimb in a controlled series of positions. These can be later assembledto provide a kinematic view of stress or impingements upon a nerveduring motion as for the ulnar nerve at the elbow.

Motion artifact can also be addressed by the software used to controlneurography system 10. In that regard, to ensure adequate echo amplitudefor MRI, the net magnetic moment generated by one pulse sequencetypically must be allowed to return near its equilibrium value beforethe next pulse sequence is initiated. This factor, in combination withthe sheer number of sequences typically required for imaging, typicallycauses data collection to occur over a relatively long period of time(e.g., on the order of 1 to 20 minutes or longer). As will beappreciated, the likelihood that significant patient motion will beexperienced during the data collection process increases in directproportion to the time required for data collection.

The software controlling the data collection processed is optimized toreduce at least some of the delays contributing to motion artifactsensitivity. In the arrangement described above, information from anumber of different images may be used to selectively produce a finalimage. For example, the subtraction neurogram is typically generated ona pixel-by-pixel (or voxel-by-voxel) basis using information from twoimages obtained with orthogonal diffusion gradients. By interleaving theimage sequence so that data for a given pixel is collected for eachdiffusional gradient before collecting data for any other pixel, thesusceptibility of the subtraction process to motion artifact is reduced.Similarly, where multiple images are collected at different gradientstrengths to compute the diffusion coefficient D for a given pixel orvoxel, as part of the gradient selection process, the susceptibility ofthe computation to motion artifact can be reduced by collecting data forall gradient strengths at one pixel before data for other pixels iscollected. Thus, despite the relatively simple data collection processdepicted in FIGS. 9 and 10, in the preferred arrangement, datacollection is interleaved by collecting data in several planes at eachacquisition rather than completely collecting all repetitions for agiven plane before proceeding to the next.

Another technique used to provide the desired motion suppression isbased upon the anticipation of certain periodic sources of motion thatcan be monitored by, for example, the auxiliary data collection system22. For example, depending upon the region of the patient underexamination, the patient's heartbeat and respiration may introduce somemotion that is not suppressed by the splint 156. With informationregarding the periodicity of these sources available from system 22,computer 72 may then adjust the data collection sequence so that theexcitation and echo pulses occur at consistent times relative to themotion introduced by the sources.

One technique for reducing respiratory motion artifact in MRI isdisclosed in U.S. Pat. No. 4,930,508 (Shimoni et al.). In contrast, theneurography system 10 can be used with a variety of techniques includingmass-spectrometer monitoring of carbon dioxide output, fiber-opticobservation at chest wall movement, or auditory monitoring by long tubestethoscope with automated sound analysis.

(c) Fascicle Identification and Nerve Enhancement

Another feature of the neurography system 10 is its ability to imageindividual nerve fascicles. For example, when a phased array coil 62, orother high resolution MRI system is used with a long TE sequence,individual nerve fascicles appear much brighter than the perineural andepineural tissue within the nerve and between the fascicles, and thenerve takes on the appearance of a multifascicled structure.

By way of illustration, neural images depicting fascicles in the nerveof a patient having a nerve graft are provided in FIGS. 20, 21, and 22.These images were obtained using a 1.5 Tesla MRI system (Signa System5.2 software release, sold by GE Medical Systems) with standard 1Gauss/cm gradients and a phased array RF coil system of the typedescribed above. A "fast" spin-echo sequence with a TR of 5000 ms, TE of102 ms, and 8 echo train was used with fat suppression and spatial RFpulses for vessel suppression.

Two axial series of images were produced using a two dimensional Fouriertransformation. The first series consisted of 24, five mm thicksections, a 512×512 matrix, one mm skip, and one nex (number ofexcitations). The second series consisted of 41, three mm thick axialsections, a 256×256 matrix, zero mm skip, and two nex. The field of viewwas 18 cm and acquisition time was 10.6 minutes for both series.

Images from the second series were post-processed by selecting(manually) an elliptical region of interest, approximately two cm indiameter, around the sciatic nerve in each of the sections. This regionof interest was selected to exclude blood vessels, without requiring theuse of the more analytically complex vessel suppression featuresdescribed above.

Projectional images were obtained using a maximum intensity projection(MIP) algorithm, available as part of System 5.t (IVI) provided by GEMedical Systems. The resultant neurograms show the interface between thetibial component of the sciatic nerve and the surgically placed suralnerve graft, with FIGS. 20 and 21 illustrating the nerve inprogressively larger scale and FIG. 22 illustrating an axial projectionof the nerve including graft g. As an additional benefit, this imagingprotocol depresses the signal from tissues within the nerve, between andamong the fascicles, so that the individual fascicles (f) of the nervestand out in sharp profile.

The ability of the neurography system 10 to image fascicles is importantfor several reasons. First, fascicle imaging enhances the diagnosticusefulness of the neurogram because it makes it possible to observe andanalyze the internal structure of the nerve for evidence of disease. Aswill be appreciated, this observation and analysis may be performedvisually by the operator or automatically, as part of the operation ofthe diagnostic system 24 described in greater detail below.

Second, this unique internal organization can be used to provide neuralselectivity and enhancement in the imaging process, even when theconspicuity or signal intensity of a particular nerve does not permitidentification. More particularly, blood vessels, lymphatics, lymphnodes and collections of adipose tissue, are often similar to nerve inshape, location, and intensity in the cross-sectional images. None ofthese features, however, exhibit the internal fascicular structure ofnerve.

By way of illustration, fascicle identification and nerve confirmationmay be used to distinguish nerve from other structures in an ambiguousimage in the following manner. First, a thresholding process is used toidentify relatively bright regions of the image potentiallyrepresentative of nerve. With the boundaries of these regionsestablished, the intensity of the pixels associated with each region isevaluated and average image intensities for the regions are computed.

If the intensity of a given pixel within a region is more than somepredetermined amount below the average intensity, the structureassociated with that pixel is a potential fascicle. A positive fascicleidentification is, however, only made if one or more of a plurality ofpredetermined sequences of such pixel groups representative offascicular structure are identified. For example, a group of at least 3such pixels must be found which are adjacent each other and bounded onat least one side by pixels not satisfying this criteria.

The results of this analysis can be used to distinguish bright regionsassociated with nerve from those associated with, for example, bloodvessels or lymphatics. The image intensity of regions not satisfying thefascicular identification parameters may then be adjusted to zero,effectively eliminating these ambiguous structures from the image.Alternatively, a neuroradiologist or other specialist may use thisinformation to select a volume of interest which the neurography systemcan then render into a projection neurogram.

(d) Miscellaneous Nerve Enhancement Techniques

The various embodiments and features of the neurography system 10described above can be modified to incorporate alternative approaches tonerve identification and enhancement.

For example, a magnetization transfer pulse sequence can be employedafter the fat suppression sequence to enhance neural imaging.Magnetization transfer involves the excitation of chemically shiftedprotons with an "off resonance" pulse. These protons in a short T₂isotropically diffusing water compartment then exchange into a long T₂anisotropically diffusing compartment. In doing so, they carry the highintensity magnetization signal with them, thus inducing a transfer ofmagnetization to surrounding neural tissue to increase its conspicuityin the image. Nerve may exhibit efficient exchange between theoff-resonance, relatively stationary protons in the myelin sheath andthe resonant, mobile protons of axoplasmic water. On the other hand,muscle does not exhibit exchange with a large off-resonant proton poolto a comparable degree. The magnetization transfer pulse sequence isdesigned to exploit this differential sensitivity between nerve andmuscle by using stimulation methods similar to fat suppression tosynergistically improve the neurographic selectivity of the image in twoways simultaneously.

Other alternative pulse sequences can also be used. For example, aversion of steady state free precession (SSFP), as described in Patz etal., The Application of Steady-State Free Precession to the Study ofVery Slow Fluid Flow, 3 MAG. RES. MED. 140-145 (1986), can be used. TheSSFP is, however, modified to be included in an imaging protocol toachieve fat suppression. Similarly, a magnetization prepared rapidgradient echo (MP-RAGE) sequence, as described in Mugler et al., ThreeDimensional Magnetization Prepared Rapid Gradient-Echo Imaging (3D MPRAGE), 15 MAG. RES. MED. 152-157 (1990) can be used if modified toimprove T₂ contrast. In addition, neural selectivity can be achieved byemploying proton fast exchange rates or T₁ relaxation rates.

Further alternative techniques for generating neurograms employsequences optimized to be sensitive to the slow coherent flow of theendoneurial fluid. These sequences provides a unique signal because ofthe proximal to distal direction of flow and because of the slow flowrate which can be monitored by techniques originally developed todistinguish diffusion from perfusion. Such techniques include, forexample, velocity compensation by gradient moment nulling as describedin Ehman et al., Flow Artifact Reduction in MRI: A Review of the Rolesof Gradient Moment Nulling and Spatial Pre-saturation, 14 MAG. RES. MED.293-307 (1990) and Moran, A Flow Velocity Zeumatographic Interface forNMR Imaging, 1 MAG. RES. IM. 197-203 (1982).

3. Medical System Construction and Operation

As noted previously, the neurography system 10 is one component of abroader medical system 12. The remaining components of system 12 aredescribed in greater detail in the following sections. These componentsprovide information to, and process information from, neurography system10 in accordance with software instructions executed by, for example, ahost processing system 32 or the processing systems of individualcomponents of the system 12 to achieve a variety of functions beyond theimaging of peripheral nerve.

a. Auxiliary Data Collection System

The auxiliary data collection system 22 may take any one of a variety ofdifferent forms. For example, as suggested above, system 22 may bedesigned to collect supplemental information regarding structure presentwithin the images produced by system 10. Examples of such systemsinclude a secondary MRI system, employing conventional pulse-echosequences suitable for use in angiography or STIR sequences to showareas of high muscle signal due to denervations or functional loss ofthe muscle; an X-ray imaging system suitable for use in generating imagedata of bone and/or tissue, a PET scanning system for showing theprogress of an axionally transported pharmaceutical agent; or a CTsystem for collecting contrast agent lymphography data. The splint 156is also formed with fiduciary markers visible using CT and MRI (e.g.,iodine contrast material in water), allowing the information fromsystems 10 and 22 to be integrated.

The supplemental information may be used to suppress structural contentin the image and provide greater neural selectivity. For example, anangiogram may be used to remove vessel image content from the neurogramor to distinguish nerve and vessels on the basis of color.Alternatively, because non-neural structure is generally absent in theimage anyway, the additional information may be employed to add specificstructures, such as blood vessels, back into the neurogramunambiguously. This process allows alternative structure to be readilydifferentiated using different colors to display information fromdifferent sources. As will be appreciated, the addition of structureinto a MR image viewed to assess neural structure was virtuallyunthinkable with prior art systems due to the low signal content ofneural return components.

An alternative type of data collection system 22 is employed to collectinformation about the patient for use in controlling the operation ofthe neurography system 10, rather than modifying its output. Examples ofsuch systems include conventional heart rate and respiration monitors,used to time the data collection sequencing of system 10 relative to theheart rate and respiration of the patient.

A final type of data collection system 22 of interest is one designed tocollect supplemental information about the neural network. For example,system 22 may be constructed to produce an output indicative of nerveconduction velocity (NCV), including the approximate location of achange in NCV or the NCV response to magnetic stimulation. Informationfrom evoked potential electrodes or magnetic SQUID detectors might alsobe collected and integrated for a multi-input display.

b. Diagnostic System

The diagnostic system 24 is selected to process the image neurograms andother information (such as D and T₂) provided by neurography system 10to provide an attending physician with, for example, diagnoses of neuralanomalies. Alternatively, system 24 may assist the physician in making adiagnosis, or assessing the need for, or likely success of, surgery. Inone embodiment, system 24 may be employed simply to confirm or questionthe physician's diagnoses.

By way of illustration, one region in which problematic neural disorderscommonly occur but are difficult to diagnose is the spinal canal. Asshown in the cross sectional view of one vertebra provided in FIG. 23,the region of interest exhibits a relatively high physiologicalcomplexity. The illustrated structures include a herniated disc (HD),compressed left spinal root (LSR), spinous process (SP), anulus fibrosus(AF), nucleus pulposus (NP), autonomic ganglion (AG), left ventral root(LVR), ventral ramus (VRA), transverse process (TP), dorsal ramus (DRA),dorsal root ganglion (DRG), facet (F), dorsal root (DRO), extradural fat(EF), root in cauda equina (RCE), dural sac (DS), and cerebrospinalfluid (CF).

In this diagram, the two features of primary interest are the leftspinal root (LSR) and the left ventral root (LVR), which are both inrisk of compression from the herniating disc (HD). Both nerves aretraveling through extradural fat (EF) but are surrounded by bone, whichcould impair observation by an X-ray based technique. Both nerves arealso near the strong water signal of the disc (HD), cerebrospinal fluid(CF) in the dural sac (DS), and other inflamed tissue (which oftendiminishes image resolution and quality in generally used magneticresonance techniques).

The left spinal root (LSR) and left ventral root (LVR) of diagnosticinterest are small relative to the numerous large anatomic structuresnearby. Also, these roots are nearly perpendicular to each other. Thiscommon imaging problem can be addressed by the use of a neurographysystem 10 programmed to employ fat suppression, followed by pulseddiffusion gradients oriented to enhance either the left spinal root(LSR) or left ventral root (LVR), so that each can be clearly seen in agiven image. If either root is compressed, its image will demonstratephysical distortion or an imprint due to the compression, which maymanifest itself as a change in structure or signal intensity between thetwo sides of a compression.

As will be appreciated, due to the selectivity and resolution of theneurograms produced by system 10, they can be evaluated by a physicianto diagnose any neural abnormalities present. In addition, the imageproduced by system 10 can be analyzed by diagnostic system 24 to detect,for example, evidence of compression or inflammation and provide theappropriate diagnosis.

The operation of system 24 depends, in part, upon the condition to beevaluated. In one embodiment, the operator initially views a two- orthree-dimensional image generated on a cathode-ray tube (CRT) display,included with system 24 and uses a cursor to identify a particularimaged nerve to be evaluated. The operator may also input the particulartypes of anomalies to be detected.

The system 24 then determines the boundaries of the imaged nerve in eachof the two dimensional images available, using a thresholding process.These boundaries can then be compared from one image to the next to lookfor discontinuities or changes in shape associated with a particularanomaly of interest. For example, if this analysis were used with anerve severed in an accident, the nerve might disappear entirely fromcertain images in which it would otherwise be expected to appear. Thesystem 24 is able to readily identify such regions and provide thephysician with precise locational information regarding the anomaly.

Similarly, the physician may be interested in conditions associated withless pronounced changes in nerve boundary or intensity. The system 24 isreadily able to provide outputs indicative of the average intensity of abounded neural area, as well as the size and shape of the bounded areaon an image-by-image basis. This information can then be used to detectanomalies such as compressions.

In one arrangement, a cursor can be used to initialize a referenceboundary of interest on the CRT (e.g., associated with a "normal" neuralcross section) for use by system 24. System 24 then compares the actualboundary of the nerve in subsequent images to the reference boundary tolocate and quantify the extent of neural compressions. Thisquantification of an anomaly then allows the physician to monitor therecovery of the nerve and assess the effectiveness of any therapy beingprovided.

Another approach that may be employed by system 24, is based upon theapparent increase in T₂ exhibited by injured nerve. More particularly,an initial "long T₂ " analysis or diffusion weighted image can beperformed to image all neural structures. Then T₂ can be extended toroughly 100 milliseconds to image only those nerves that are injured.

An additional approach for use in the manual evaluation of, for example,bone fractures and injuries to joints, involves the analysis of an imagein which the fat component is selectively demonstrated and remainingtissues suppressed. This approach emphasizes the appearance of skin,adipose collections, and of bone (in many locations) due to the presenceof marrow. When such an image is collected and assigned a color otherthan that used to display nerve, the two images can be showntransparently in the same three dimensional construction. As a result,the physician is provided with useful information regarding the physicalrelationship between nerves and bones. This information is mostimportant in the evaluation and treatment of bone fractures and jointinjuries.

In some applications, a contrast agent may be used to synergisticallyhighlight the anomaly of interest. Alternatively, because nerves appearbright and isolated in an image, it may be more informative toselectively black out one of the nerves by means of administering anintraneural pharmaceutical contrast agent.

In addition to analyzing the output of neurography system 10, thediagnostic system 24 may also provide feedback to system 10 to controlthe pulse sequences used and the type of information produced. Forexample, where sites of nerve compression, section, laceration, orfibrosis are imaged, the alteration in endoneurial fluid flow and inaxoplasmic flow are readily detected by monitoring the increase insignal intensity when T₂ -based, or other, neurographic sequences areused.

Although not described in detail herein, a variety of differentdiagnostic applications are contemplated including:

1. The demonstration of a patient's peripheral, cranial, and autonomicnerve and nerve plexus anatomy.

2. The demonstration of a patient's spinal root anatomy, particularlythe cervical, thoracic and lumbar spinal roots and nerves where theypass through fat at the foramina through which they exit the spinalcanal.

3. The demonstration of a patient's spinal root anatomy within thelumbar canal where the roots pass through quantities of extradural fat.

4. The examination of a patient's cranial nerves for compressions byvessels or other structures which could cause trigeminal neuralgia (Vthnerve), hemifacial spasm or Bell's palsy (VIIth nerve), essentialhypertension (Xth nerve) or other cranial nerve syndromes.

5. The demonstration of nerve, plexus or root compressions or injuriesin a patient, where abnormal changes in the direction, position, orother diffusional properties are caused by an injurious process, such asnerve transection, demyelinating diseases, neuritis, multiple sclerosis,peripheral neuropathies and crush injuries, as well as the monitoring ofthe regrowth of nerves.

6. The determination of the location of tumors or other masses withinthe spinal cord where it is useful to know the position ofcortico-spinal motor tracts or other functional white matter long tractsrelative to some abnormality.

7. Demonstrating the anatomy of the optic nerve, an extension of thebrain, where it passes through the peri-orbital fat or other fat on itsroute to the retina.

8. Tract tracing within the brain to provide useful images for study byradiologists, surgeons or physicians and, in particular, foridentification of the location of areas of `eloquent cortex` such as themotor strip, or speech-related areas. This method involves the spatialidentification of relevant areas of the thalamus or internal capsule andthen following projecting tracts to the area of interest on the corticalsurface, or identifying regions of interest by reference to theirconnections with other areas on the cortical surface. For example,speech cortex projection tracts can be followed from areas known to beinvolved in speech production to (and through) other areas where aninjury or stroke may have blocked proper nerve function.

9. Tracing of nerves as they pass through tumors of low diffusionalanisotropy, such as the passage of the VIIth nerve through an acousticneuroma to permit a surgeon to know the location of the nerve in or nearthe tumor and so to have the ability to avoid the nerve during surgeryon the tumor.

10. Application of diffusion anisotropy imaging for the evaluation ofdiffuse axonal injury, as may occur in head injury.

11. The evaluation of bone fractures and joint dislocations ordislocation/fractures in which surgical planning, management andfixation would benefit from knowing the course of the nerve in theregion of the abnormal anatomy.

c. Therapeutic System

As noted previously, a therapeutic system 26 is also employed to processinformation from neurography system 10 or other components of medicalsystem 12 to better effect the administration of therapy to the patient.For example, system 26 may be a drug-delivery system or acurrent-stimulation system that employs feedback from neurography system10 to regulate its operation. In this fashion, more precise nerveconduction velocity (NCV) or evoked potential tests can be done usingneurographic data to place stimulating or recording electrodes. Fortherapy, tract information could aid in the placement of transplanttissue or for lesions of areas of abnormal activity that might causetremor in the thalamus.

d. Surgical System

The surgical system 28 employs neurographic information from system 10to influence any one of a variety of surgical operations that may beperformed. The information obtained may be used to avoid neural pathsduring surgery or to confirm the location and nature of neural surgeryrequired. The operation of surgical system 28 may be automaticallycontrolled in response to feedback from system 10 or may manuallycontrolled by a surgeon based upon his or her review of the informationprovided.

In one embodiment, the region of the patient that is to undergo surgeryis placed in the splint 156 described above. The open regions 170 ofsplint 156 need to be designed and positioned to ensure that splint 156will not interfere with the surgical system 28 during the performance ofan operation. With the splint 156 applied, image data is then collectedvia the neurography system 10. As noted previously, the processingsystem 16 of neurography system 10 provides numerical coordinates, inthree dimensions, describing the position of the nerves along theircourses with reference to the splint base 158 and fiduciary markers 162.

Depending upon the nature of the operation to be performed, outputs fromthe auxiliary data collection system 22 may also be required. Forexample, if system 28 is employed to operate on bone within the regionimaged, system 22 may be called upon to generate a fat selective imageof the bone, or the patient may be brought to a C-T scanner forpreparation of a bone image. The MRI splint 156 will be worn while thisadditional information is collected, but additional markers (e.g., chalkor iodine solution for CT X-rays) are required to extract locationalinformation from the secondary image and, hence, provide the requisiteregistration between the two images generated.

The image information is loaded into the memory of a surgical systemprocessor 178, shown in FIG. 24. As will be described in greater detailbelow, in the preferred arrangement, processor 178 is programmed toguide surgical operations performed in a coordinate system that isreferenced to the image coordinate system. The base 158 of splint 156 issecured to a platform 180 included in surgical system 28 to provide afixed relationship between the coordinate systems used in the image andby system 28. The coordinate systems are then linked by processor 178using a computer model of three-dimensional space. Confirmatory X-raysmay be taken conveniently during the procedure to assure correctpositioning of the markers.

An articulated surgical arm 182 is coupled to the platform 180 and has astylus 184 (e.g., a surgical apparatus, such as a focused laser beam ora drill) provided at its free end. The arm 182 can be moved electricallyor pneumatically to any select point, or along any select path, definedrelative to the operating environment in response to outputs fromprocessor 178. The position of the arm 182 can, thus, be tracked via thecontrol outputs applied by the processor 178. As will be appreciated, aseparate coordinate-based or laser-based positioning system may beemployed if desired.

In the preferred embodiment, the position of the stylus relative to theimaged neural and other networks is illustrated on a system display 186during the course of a surgical operation. The surgeon may manuallyguide the stylus 184 during the operation via, for example, a joystick,electronic glove, or other input device 188, visually monitoring theposition of the stylus relative to anatomical structure. This visualfeedback may be based simply upon a comparison of the known positionalrelationship of the stylus to the previously collected image.

Alternatively, it is possible to obtain visual confirmation usingimaging feedback data collected in real time. For instance, with the useof high-speed MRI data collection sequences, such as echo planar imagingdescribed in Worthington et al., The Clinical Applications of EchoPlanar Imaging in Neuroradiology, 32 NEURORADIOLOGY 367-370 (1990), itis possible to rapidly update images. When the resulting images aredisplayed, the surgeon may observe the progression of an appropriatelylabeled, non-magnetic probe into the body in real time. If a slowerimage collection process is employed, the probe or device is advanced insteps as a series of images are taken. In either case, the neurographicimage provides the surgeon with apparent vision of sensitive neuraltissue inside opaque, solid body structures, in much the same manner asfluoroscopy, but while also providing information regarding neuralpaths.

As an alternative to requiring the surgeon to control the operation ofsystem 28 during surgery, a computer-guided, stereotaxic, or fiduciarysystem may be employed. In that regard, the surgeon may provideprocessor 178 with input identifying the nature of the operation to beperformed, including the stylus path and operation appropriate for thesurgery to be performed. These steps can be performed with the arm 182disengaged, allowing the surgeon to simulate the operation and view thestylus path on the image, before the surgical procedure is confirmed.Once confirmed, the processor 178 can then be instructed toautomatically guide the arm 182 over the desired path during the actualsurgical operation.

The use of surgical system 28 has a number of important advantages overthe "neurally blind" surgical methods currently employed. For example,because nerves are readily imaged, the surgeon is better able to assessany neural conditions that might require treatment or alter the surgicalplan. In addition, because the position of the stylus 182 relative tonerve is readily imaged and can be confirmed before an operation isperformed, accidental intrusion of the stylus upon neural paths isavoided.

Although a splint 156 is employed in the embodiment described above toprovide a link between the referential frames of the neurogram andsurgical system 28, it is not mandatory. For example, particularly inregions that are relatively unsusceptible to motion artifact, fiduciarymarkers can be applied directly to the body (e.g., on the head or facewhen nerves of facial sensation or movement are involved, or adjacentthe iliac crests and lumbar vertebral spinous processes when lumbarnerve roots are involved).

The use of a computer-guided surgical system 28 employing such fiduciarymarkers is believed to be of particular importance in cervical, thoracicor lumbar spine surgery. In that regard, system 28 will eliminate theproblem of doing a "good" operation but at the wrong level, e.g.,inadvertently decompressing the lumbar 3/4 root when the symptomaticcompression to be relieved was actually at the lumbar 4/5 root. Forspine work, the original image can be collected with a strip offiduciary markers taped to the patient's back and independently markedin a manner that can be sensed by system 28 to allow location of thestylus 182 during surgery.

As will be appreciated, the various components of the surgical system 28can be altered in a variety of manners. For example, the stylus 182 mayinclude a surface detector of electrical fields or a magnetic detectorof nerve activity, constructed to detect the activity of nerves.Examples of such devices include a somatosensory evoked potential ormagneto-encephalography system. As a result, the detection of nervesoffered by embodiment of the stylus 182 allows the position of thestylus relative to nerve determined by reference to the neurogram to beconfirmed.

One application for surgical system 28 that is of particular importanceis in surgery of the neck. Surgery of this type includes, for example, acarotid endarterectomy to remove stroke producing plaque from theinternal carotid artery, an anterior cervical discectomy to relieve acervical root compression, or an operation for cancer in the neck. Oneof the most common complications of such surgery is the accidentalcrushing or transection of the recurrent laryngeal nerve, possiblyresulting in permanent paralysis of one or both sets of vocal cords.Optimally, a preoperative neurographic image is used to demonstrate thecourse of the recurrent laryngeal nerve, allowing the surgeon to moreeffectively avoid it or at least identify and protect it during surgery.

Neurographic guidance can be also used for percutaneous needle biopsy oflesions, or for the placement of more elaborate percutaneous systemssuch as ultrasonic or other mechanical devices used to remove tissues.By way of illustration, such operations include discectomies, theintroduction of laser/suction systems, the placement of RF lesioningdevices used in procedures such as gangliolysis of the fifth cranialnerve, the placement of probes to carry out deep tissue localized drugadministration, diathermy, cryotherapy, or other physical or mechanicaltechniques. Neurographic guidance may also be used to control thepassage of rigid endoscopes through solid tissues or to guide theplacement of directable flexible endoscopes.

Yet another important application of surgical system 28 is in the use ofCNS neurograms to guide stereotactic surgery in the brain. Currently,tissue structures visible by virtue of their T₁ or T₂ MRI are used toguide stereotactic surgery. In contrast, CNS neurograms provideinformation concerning the connections or relation of specific tracts ofinterest, which may travel in or among other tracts from which theycannot be differentiated by means of conventional tissue-based images.

e. Training and Development System

The training and development system 30 may take any one of a variety offorms designed to process information collected from the neurographysystem 10. In one embodiment, neurographic images are collected from aplurality of patients to produce an anatomical atlas of normal andabnormal neural paths for reference by surgeons and others. Imagesobtained from a patient can be compared to the clinically knownpopulation in the atlas to rapidly identify anomalous nerve courses in apatient set to undergo surgery for some condition. As a result, thesurgeon may be able to modify his or her technique to reduce the risk ofinjury to nerves which happen to be in the field of surgery. Similarly,a neurographic map of an individual patient's skin and cutaneous nervescan be used to help the surgeon plan incisions that avoid the verycommon complication of accidental transection of cutaneous nerves in thecourse of routine surgical incision to reach structures below the skin.

In another embodiment, the training and development system 30 may bedesigned to assess the effectiveness of the programming employed byneurography system 10 and may provide feedback to system 10 to regulateits operation and enhance the quality of the neurograms generated. Moreparticularly, once a sequence able to positively identify nerve has beenemployed, alternative sequences can be employed and their resultscompared to the confirmed method. As a result, a collection oftechniques can be established and programmed into neurography system,along with the conditions under which each sequence offers the bestperformance.

Another alternative training and development system 30 may be employedto assess the effectiveness of intraneural, pharmaceutical contrastagents designed to help in the diagnosis of nerve compressions. Moreparticularly, such a training system 30 is used to quantify the imagecontrast produced using different contrast agents to image known neuralanomalies. As a result, system 30 is able to identify those agentsproviding the best results for particular neural imaging problems.

Yet another embodiment of training and development system 30 allowsinformation from neurograms to be used in the design of any of a varietyof products. For example, the neurograms produced by system 10 provideinformation that is of great advantage to designers of ergonomicfurniture, high gravity air or space craft seats, specialized bodysuits, boots, and various kinds of electronic or electric medicalequipment, which can be best used when the positions of nerves can beprecisely located in advance. The system 30 incorporates informationregarding neural paths from system 10 into the computational processesinvolved in designing such equipment to provide enhanced productperformance.

As one illustration, in the ergonomic design of a chair, system 30 wouldbe programmed to ensure that the primary support provided by the chairdoes not coincide with a neural path in the relevant customerpopulation. This can be done by superimposing the neural network of asitting person upon a mathematical model of the chair, identifying theprimary points of support, and generating flags on the display for anysupport points that are within some predetermined distance of a nerve.As a result, the chair design can be manipulated to avoid neuralcompressions.

In another application, the system 30 can be used to control electronicprosthesis. More particularly, the information from system 10 can beused to locate electronic detectors adjacent, for example, a severednerve to detect neural activity associated with the limb replaced by theprosthesis. The detected activity of the nerve is then used to controlthe prothesis.

4. Non-neural Imaging Applications

In principle, selective imaging of any other object or subject may beeffected using the MRI techniques described above, if that subject orobject exhibits characteristics corresponding to the neuralcharacteristics described above. For example, objects exhibitingdiffusion anisotropy in any part thereof can be imaged using diffusionalweighting. Thus, in medicine, for example, the cardiovascular systemcould also be imaged in this fashion and the technique can also beemployed to examine, for example, rock strata and plants, if theyexhibit diffusion anisotropy.

5. Conclusion

The lack of a suitable method for creating a distinct image of thenerves has been a great hindrance to physicians, surgeons, athletictrainers, and pain treatment specialists. Although, previously, it hassometimes been possible to make a nerve stand out from immediatelysurrounding structures, the unique ability of system 10 to make thenerve stand out from all other structures represents a significantadvance. The sensitivity of the system 10 allows even the smallestnerves to be accurately identified and linked to form three-dimensionalprojections of a neural network. The neurographic information can becollected rapidly, without requiring contrast agents.

While the preferred embodiment of the invention has been illustrated anddescribed, it will be appreciated that various changes can be madetherein without departing from the spirit and scope of the invention.

The embodiments of the invention in which an exclusive property orprivilege is claimed are defined as follows:
 1. A method of utilizingmagnetic resonance to determine the shape and position of mammal tissue,said method including the steps of:(a) exposing an in vivo region of asubject to a magnetic polarizing field, the in vivo region includingnon-neural tissue and a nerve, the nerve being a member of the groupconsisting of peripheral nerves, cranial nerves numbers three throughtwelve, and autonomic nerves; (b) exposing the in vivo region to anelectromagnetic excitation field; (c) sensing a resonant response of thein vivo region to the polarizing and excitation fields and producing anoutput indicative of the resonant response; (d) controlling theperformance of the steps (a), (b), and (c) to enhance, in the outputproduced, the selectivity of said nerve, while the nerve is living inthe in vivo region of the subject; and (e) processing the output togenerate a data set describing the shape and position of said nerve,said data set distinguishing said nerve from non-neural tissue, in thein vivo region to provide a conspicuity of the nerve that is at least1.1 times that of the non-neural tissue, without the use of neuralcontrast agents, said processing including the step of analyzing saidoutput for information representative of fascicles found in peripheralnerves, cranial nerves numbers three through twelve, and autonomicnerves.
 2. The method of claim 1, wherein the step of processing furtherincludes using the results of said step of analyzing the output forinformation representative of fascicles to suppress from said data settissue that is not fascicular.
 3. A method of utilizing magneticresonance to determine the shape and position of mammal tissue, saidmethod including the steps of:(a) exposing an in vivo region of asubject to a magnetic polarizing field, the in vivo region includingnon-neural tissue and a nerve, the nerve being a member of the groupconsisting of peripheral nerves, cranial nerves numbers three throughtwelve, and autonomic nerves; (b) exposing the in vivo region to anelectromagnetic excitation field; (c) sensing a resonant response of thein vivo region to the polarizing and excitation fields and producing anoutput indicative of the resonant response; (d) controlling theperformance of the steps (a), (b), and (c) to enhance, in the outputproduced, the selectivity of said nerve, while the nerve is living inthe in vivo region of the subject, said step of controlling theperformance of steps (a), (b), and (c) including selecting a combinationof echo time and repetition time that exploits a characteristicspin-spin relaxation coefficient of peripheral nerves, cranial nervesnumbers three through twelve, and autonomic nerves, wherein saidspin-spin relaxation coefficient is substantially longer than that ofother surrounding tissue; and (e) processing the output to generate adata set describing the shape and position of said nerve, said data setdistinguishing said nerve from non-neural tissue, in the in vivo regionto provide a conspicuity of the nerve that is at least 1.1 times that ofthe non-neural tissue, without the use of neural contrast agents.
 4. Themethod of claim 3, wherein the step of selecting said combination ofecho time and repetition time includes selection of an echo time that isgreater than 60 milliseconds to enhance the distinction of said nervefrom non-neural tissue in the in vivo region.
 5. The method of claim 4,further comprising the step of repeating said step of exposing the invivo region to an excitation field after a repetition time that isgreater than one second to enhance the distinction of said nerve fromthe non-neural tissue in the in vivo region.
 6. The method of claim 4,wherein the non-neural tissue includes fat and said method furthercomprises exposing the in vivo region to electromagnetic fields thatsuppress the contribution of the fat in said output prior to producingan output at step (c).
 7. A method of utilizing magnetic resonance todetermine the shape and position of mammal tissue, said method includingthe steps of:(a) exposing an in vivo region of a subject to a magneticpolarizing field, the in vivo region including non-neural tissue and anerve, the nerve being a member of the group consisting of peripheralnerves, cranial nerves numbers three through twelve, and autonomicnerves, said magnetic polarizing field including a firstdiffusion-weighted gradient that is substantially parallel to the nerveand a second diffusion-weighted gradient that is substantiallyperpendicular to the nerve; (b) exposing the in vivo region to anelectromagnetic excitation field; (c) sensing a resonant response of thein vivo region to the polarizing and excitation fields and producing afirst output indicative of the resonant response to said firstdiffusion-weighted gradient and a second output indicative of theresponse to said second diffusion-weighted gradient; (d) controlling theperformance of the steps (a), (b), and (c) to enhance, in the outputproduced, the selectivity of said nerve, while the nerve is living inthe in vivo region of the subject; and (e) subtracting said first outputfrom said second output to generate a data set describing the shape andposition of said nerve, said data set distinguishing said nerve fromnon-neural tissue, in the in vivo region to provide a conspicuity of thenerve that is at least 1.1 times that of the non-neural tissue, withoutthe use of neural contrast agents.
 8. The method of claim 7, wherein thestep of subtracting further includes the step of determining aregistration between the first output and the second output.
 9. Themethod of claim 8, wherein said method includes the step of inhibitingthe step of subtracting unless a threshold level of registration isexhibited between the first and second outputs.
 10. The method of claim7, wherein the non-neural tissue includes fat, and wherein the methodincludes the step of exposing the in vivo region to electromagneticfields that suppress the contribution of the fat in said first andsecond outputs prior to the steps exposing the in vivo region to saidfirst and second gradients.
 11. A method of utilizing magnetic resonanceto determine the shape and position of mammal tissue, said methodincluding the steps of:(a) exposing an in vivo region of a subject to amagnetic polarizing field that includes a predetermined arrangement ofdiffusion-weighted gradients, the in vivo region including non-neuraltissue and a nerve, the nerve being a member of the group consisting ofperipheral nerves, cranial nerves numbers three through twelve, andautonomic nerves; (b) exposing the in vivo region to an electromagneticexcitation field; (c) sensing a resonant response of the in vivo regionto the polarizing and excitation fields and producing an outputindicative of the resonant response, said producing an output indicativeof the resonant response including the step of producing a separateoutput for each diffusion-weighted gradient of said predeterminedarrangement of diffusion-weighted gradients; (d) controlling theperformance of the steps (a), (b), and (c) to enhance, in the outputproduced, the selectivity of said nerve, while the nerve is living inthe in vivo region of the subject; (e) processing the output to generatea data set describing the shape and position of said nerve, said dataset distinguishing said nerve from non-neural tissue, in the in vivoregion to provide a conspicuity of the nerve that is at least 1.1 timesthat of the non-neural tissue, without the use of neural contrastagents, said processing the output including the step of vectorprocessing the separate outputs for each said diffusion-weightedgradient of said predetermined arrangement of diffusion-weightedgradients to generate data representative of anisotropic diffusionexhibited by the nerve, and processing said data representative of saidanisotropic diffusion to generate said data set describing the shape andposition of the nerve.
 12. A method of utilizing magnetic resonance todetermine the shape and position of mammal tissue, said method includingthe steps of:(a) exposing an in vivo region of a subject to a magneticpolarizing field, the in vivo region including non-neural tissue thatincludes fat and a nerve, the nerve being a member of the groupconsisting of peripheral nerves, cranial nerves numbers three throughtwelve, and autonomic nerves; (b) exposing the in vivo region to anelectromagnetic excitation field; (c) sensing a resonant response of thein vivo region to the polarizing and excitation fields and producing anoutput indicative of the resonant response; (d) controlling theperformance of the steps (a), (b), and (c) to enhance, in the outputproduced, the selectivity of said nerve, while the nerve is living inthe in vivo region of the subject; and (e) processing the output togenerate a data set describing the shape and position of said nerve,said data set distinguishing said nerve from non-neural tissue, in thein vivo region to provide a conspicuity of the nerve that is at least1.1 times that of the non-neural tissue, without the use of neuralcontrast agents; andsaid steps of exposing the in vivo region to anexcitation field and producing an output being designed to suppress thecontribution of fat in the output, said step of processing the output togenerate the data set including the step of analyzing the output forinformation representative of fascicles found in peripheral nerves,cranial nerves numbers three through twelve and autonomic nerves. 13.The system of claim 12, wherein the contribution of fat is suppressed byemploying a chemical shift selective sequence.
 14. The method of claim12, wherein the step of processing further includes using the results ofsaid step of analyzing the output for information representative offascicles to suppress from said data set tissue that is not fascicular.15. A method of utilizing magnetic resonance to determine the shape andposition of mammal tissue, said method including the steps of:(a)exposing an in vivo region of a subject to a magnetic polarizing field,the in vivo region including non-neural tissue that includes bloodvessels and a nerve, the nerve being a member of the group consisting ofperipheral nerves, cranial nerves numbers three through twelve, andautonomic nerves; (b) exposing the in vivo region to an electromagneticexcitation field; (c) sensing a resonant response of the in vivo regionto the polarizing and excitation fields and producing an outputindicative of the resonant response; (d) performing the steps (a), (b),and (c) to produce a second output in which the conspicuity of bloodvessels is enhanced; and (e) processing said output indicative of theresonant response and said second output to generate a data set in whichconspicuity of the blood vessels is suppressed, said data set describingthe shape and position of said nerve and distinguishing said nerve fromnon-neural tissue, in the in vivo region to provide a conspicuity of thenerve that is at least 1.1 times that of the non-neural tissue, withoutthe use of neural contrast agents.
 16. A method of utilizing magneticresonance to determine the shape and position of mammal tissue, saidmethod including the steps of:(a) exposing an in vivo region of asubject to a magnetic polarizing field, the in vivo region includingnon-neural tissue and a nerve, the nerve being a member of the groupconsisting of peripheral nerves, cranial nerves numbers three throughtwelve, and autonomic nerves; (b) exposing the in vivo region to anelectromagnetic excitation field; (c) sensing a resonant response of thein vivo region to the polarizing and excitation fields and producing anoutput indicative of the resonant response; (d) controlling theperformance of the steps (a), (b), and (c) to enhance, in the outputproduced, the selectivity of said nerve, while the nerve is living inthe in vivo region of the subject; and (e) processing the output togenerate a data set describing the shape and position of said nerve,said data set distinguishing said nerve from non-neural tissue, in thein vivo region to provide a conspicuity of the nerve that is at least1.1 times that of the non-neural tissue, without the use of neuralcontrast agents; wherein said steps (a) through (c) include the step ofexposing the in vivo region to a readout gradient rephasing pulse and aslice-selective excitation pulse, said readout gradient rephasing pulsebeing generated directly before said output pulse is produced instead ofdirectly after the generation of the slice-selective excitation pulse,so as to reduce the appearance of undesirable cross-terms in said dataset.
 17. The method of claim 16, wherein said steps (a) through (c)further include the step of exposing the in vivo region to a two-partphase encoding gradient, so as to further reduce the appearance ofundesirable cross-terms in said data set.
 18. A method of utilizingmagnetic resonance to determine the shape and position of mammal tissue,said method including the steps of:(a) exposing an in vivo region of asubject to a magnetic polarizing field, the in vivo region includingnon-neural tissue and a nerve, the nerve including epineurium andperineurium and being a member of the group consisting of peripheralnerves, cranial nerves numbers three through twelve, and autonomicnerves; (b) exposing the in vivo region to an electromagnetic excitationfield; (c) sensing a resonant response of the in vivo region to thepolarizing and excitation fields and producing an output indicative ofthe resonant response; (d) controlling the performance of the steps (a),(b), and (c) to enhance, in the output produced, the selectivity of saidnerve, while the nerve is living in the in vivo region of the subject;and (e) processing the output to generate a data set describing theshape and position of said nerve, said data set distinguishing saidnerve from non-neural tissue, in the in vivo region to provide aconspicuity of the nerve that is at least 1.1 times that of any adjacentnon-neural tissue, without the use of neural contrast agents.
 19. Themethod of claim 18, wherein said data set distinguishes said nerve fromnon-neural tissue in the in vivo region so that said data set describesthe nerve at an intensity at least 5 times that of the non-neuraltissue.
 20. The method of claim 18, wherein the step of exposing the invivo region to a polarizing field includes the step of exposing the invivo region to a polarizing field including at least onediffusion-weighted gradient.
 21. The method of claim 20, wherein the atleast one diffusion-weighted gradient includes a first gradientsubstantially parallel to the nerve and a second gradient substantiallyperpendicular to the nerve, and the step of producing an output includesthe steps of producing a first output when the first gradient isemployed and a second output when the second gradient is employed, andthe step of processing the output includes the step of subtracting thefirst output from the second output.
 22. The method of claim 20, whereinthe at least one diffusion-weighted gradient includes a predeterminedarrangement of gradients, the step of producing an output includes thestep of producing a separate output associated with each gradient, andthe step of processing the output includes the steps of vectorprocessing the separate outputs to generate data representative ofanisotropic diffusion exhibited by the nerve, and processing said datarepresentative of anisotropic diffusion to generate said data setdescribing the shape and position of the nerve.
 23. The method of claim18, wherein the non-neural tissue includes fat, and the steps ofexposing the in vivo region to an excitation field and producing anoutput involve the excitation of any fat in the in vivo region in amanner designed to suppress the contribution of the fat to the output.24. The method of claim 23, wherein the step of processing furtherincludes the step of analyzing the output for information representativeof fascicles found in peripheral nerves, cranial nerves numbers threethrough twelve, and autonomic nerves.
 25. The method of claim 18,wherein step (d) includes the step of selecting a combination of echotime and repetition time that exploits a characteristic spin-spinrelaxation coefficient of peripheral nerves, cranial nerves numbersthree through twelve, and autonomic nerves, said spin-spin relaxationcoefficient of these nerves being substantially longer than that ofother surrounding tissue.
 26. The method of claim 18, wherein step (d)includes the step of controlling said step (b) to expose the in vivoregion to an excitation field that induces a magnetization transfer fromnon-anisotropically diffusing water in the in vivo region toanisotropically diffusing water in said nerve, to more readilydistinguish the nerve from non-neural tissue.
 27. The method of claim26, wherein the non-neural tissue includes fat and said method furthercomprises exposing the in vivo region to electromagnetic fields thatsuppress the contribution of the fat in said output prior to producingan output at step (c).
 28. The method of claim 18, wherein the in vivoregion includes blood vessels and said step (d) suppresses the bloodvessels from said data set.
 29. The method of claim 28, wherein theconspicuity of nerve is enhanced in said output and said steps (a), (b),and (c) are performed a second time to produce a second output in whichthe conspicuity of blood vessels is enhanced and wherein said step (e)of processing the output includes the step of processing said output andsaid second output to suppress the blood vessels from said data set. 30.The method of claim 18, wherein if the non-neural tissue in said in vivoregion includes blood vessels and cerebrospinal fluid, said step (d)includes the step of selecting the polarizing field of step (a) and theexcitation field of step (b) to suppress the blood vessels and thecerebrospinal fluid from said data set.
 31. The method of claim 18,wherein said step (c) includes the step of processing said output on aninterleaved pixel-by-pixel basis to suppress the influence of motion ofthe in vivo region on said data set.
 32. The method of claim 18, whereinsaid method further includes the step of immobilizing the in vivo regionin a splint to reduce motion artifact in said data set.
 33. The methodof claim 18, wherein the in vivo region includes a plurality ofperipheral nerves, cranial nerves numbers three through twelve, orautonomic nerves, and said method further includes the step ofadministering a contrast agent to a selected one of the plurality ofperipheral nerves, cranial nerves numbers three through twelve, orautonomic nerves to remove said selected one nerve from said data set.34. The method of claim 18, wherein the intensity of said nerve in saiddata set is at least 10 times that of non-neural tissue in the in vivoregion.
 35. The method of claim 18, wherein said method further includesthe step of processing said data set to generate an image displaying theshape and position of said nerve.
 36. A method of utilizing magneticresonance to determine the shape and position of a structure, saidmethod including the steps of:(a) exposing a region to a magneticpolarizing field including a predetermined arrangement ofdiffusion-weighted gradients, the region including a selected structurethat exhibits diffusion anisotropy and other structures that do notexhibit diffusion anisotropy; (b) exposing the region to anelectromagnetic excitation field; (c) for each of saiddiffusion-weighted gradients, sensing a resonant response of the regionto the excitation field and the polarizing field including thediffusion-weighted gradient and producing an output indicative of theresonant response; and (d) vector processing said outputs to generatedata representative of anisotropic diffusion exhibited by said selectedstructure in the region, regardless of the alignment of saiddiffusion-weighted gradients with respect to the orientation of saidselected structure; and (e) processing said data representative ofanisotropic diffusion to generate a data set describing the shape andposition of said selected structure in the region, said data setdistinguishing said selected structure from other structures in theregion that do not exhibit diffusion anisotropy.
 37. The method of claim36, wherein said selected structure is neural tissue in a mammal andsaid other structures are non-neural tissue in the mammal.
 38. Themethod of claim 37, wherein said step of processing said datarepresentative of anisotropic diffusion includes the steps of:analyzingsaid data representative of anisotropic diffusion to determine aneffective direction of the anisotropic diffusion exhibited by saidneural tissue, so as to determine an optimal orientation fordiffusion-weighted gradients; exposing the region to two additionaldiffusion-weighted gradients respectively substantially parallel to andsubstantially perpendicular to said effective direction; producing twoadditional outputs indicative of the region's resonant responsesrespectively to said two additional diffusion-weighted gradients; andcalculating a difference between said two additional outputs to generatesaid data set describing the shape and position of said neural tissue.39. The method of claim 37, wherein said data set describing the shapeand position of said neural tissue describes the shape and position of aselected cross section of said neural tissue, and the steps used togenerate said data set are repeated to generate additional data setsdescribing different cross sections of said neural tissue, and a furtherdata set that describes the three dimensional shape and position of asegment of said neural tissue is generated by steps including:analyzingthe data representative of anisotropic diffusion to determine how torelate said data set and said additional data sets describing the shapeand position of cross sections of said neural tissue; and based upon theresults of said step of analyzing the data representative of anisotropicdiffusion, combining said data set and said additional data sets togenerate said further data set that describes the three dimensionalshape and position of the segment of said neural tissue, therebyenabling the three dimensional shape and position of curved neuraltissue to be described.
 40. The method of claim 39, wherein said step ofanalyzing the data representative of anisotropic diffusion includesdetermining an effective direction of the anisotropic diffusionexhibited by said neural tissue in each of said selected and differentcross sections.
 41. The method of claim 37, wherein said predeterminedarrangement of gradients includes first, second, and third orthogonalgradients, and said data representative of anisotropic diffusion includea description of an effective vector representative of the anisotropicdiffusion exhibited by said neural tissue.
 42. The method of claim 41,wherein said data set describing the shape and position of said neuraltissue is based upon the length of said effective vector.
 43. The methodof claim 42, wherein the step of exposing the region to a magneticpolarizing field includes the step of exposing the region to a zerodiffusion gradient polarizing field that does not include adiffusion-weighted gradient, the step of producing an output includesthe step of producing a zero diffusion gradient output indicative of theregion's resonant response to said zero diffusion gradient polarizingfield, and the length of said effective vector is normalized by amagnitude of said zero diffusion gradient output.
 44. The method ofclaim 41, wherein said data set describing the shape and position ofsaid neural tissue is based upon an angle describing in part thedirection of said effective vector.
 45. The method of claim 41, whereinsaid step of processing said data representative of anisotropicdiffusion includes the steps of:exposing the region to two additionaldiffusion-weighted gradients respectively substantially parallel to andsubstantially perpendicular to the direction of said effective vectorrepresentative of the anisotropic diffusion exhibited by said neuraltissue; producing two additional outputs indicative of the region'sresonant responses respectively to said two additionaldiffusion-weighted gradients; and calculating a difference between saidtwo additional outputs to generate said data set describing the shapeand position of said neural tissue.
 46. The method of claim 41, whereinsaid data set describes the shape and position of a selected crosssection of said neural tissue, and the steps used to generate said dataset are repeated to generate additional data sets describing differentcross sections of said neural tissue, and a further data set thatdescribes the three dimensional shape and position of a segment of saidneural tissue is generated by steps including:analyzing the datarepresentative of anisotropic diffusion to determine how to relate saiddata set and said additional data sets describing the shape and positionof cross sections of said neural tissue; and based upon the results ofsaid step of analyzing the data representative of anisotropic diffusion,combining said data set and said additional data sets to generate saidfurther data set that describes the three dimensional shape and positionof the segment of said neural tissue, thereby allowing the threedimensional shape and position of curved neural tissue to be described.47. The method of claim 46, wherein said step of analyzing the datarepresentative of anisotropic diffusion includes the step of analyzingthe direction of the effective vector representative of the anisotropicdiffusion exhibited by said neural tissue in each of said crosssections.
 48. The method of claim 46, wherein said step of processingsaid data representative of anisotropic diffusion includes the stepsof:analyzing said data representative of anisotropic diffusion todetermine an effective direction of the anisotropic diffusion exhibitedby said selected structure, so as to determine an optimal orientationfor diffusion-weighted gradients; exposing the region to two additionaldiffusion-weighted gradients respectively substantially parallel to andsubstantially perpendicular to said effective direction; producing twoadditional outputs indicative of the region's resonant responsesrespectively to said two additional diffusion-weighted gradients; andcalculating a difference between said two additional outputs to generatesaid data set describing the shape and position of said selectedstructure.
 49. The method of claim 36, wherein said data set describingthe shape and position of said selected structure describes the shapeand position of a selected cross section of said selected structure, andthe steps used to generate said data set are repeated to generateadditional data sets describing different cross sections of saidselected structure, and a further data set that describes the threedimensional shape and position of a segment of said selected structureis generated by steps including:analyzing the data representative ofanisotropic diffusion to determine how to relate said data set and saidadditional data sets describing the shape and position of cross sectionsof said selected structure; and based upon the results of said step ofanalyzing the data representative of anisotropic diffusion, combiningsaid data set and said additional data sets to generate said furtherdata set that describes a three dimensional shape and position of thesegment of said selected structure, thereby allowing the threedimensional shape and position of a curved structure exhibitinganisotropic diffusion to be described.
 50. The method of claim 36,wherein said predetermined arrangement of gradients includes first,second, and third orthogonal gradients, and said data representative ofanisotropic diffusion include a description of an effective vectorrepresentative of the anisotropic diffusion exhibited by said selectedstructure.
 51. A method of utilizing magnetic resonance to determinedata representative of diffusion anisotropy exhibited by a structure,said method including the steps of:(a) exposing a region to asuppression sequence of electromagnetic fields that suppresses theelectromagnetic responsiveness of structures in the region that do notexhibit diffusion anisotropy, so as to increase the apparent diffusionanisotropy of structures in the region that exhibit diffusionanisotropy, said suppression sequence of electromagnetic fields notincluding diffusion-weighted magnetic gradients; (b) exposing the regionto a predetermined arrangement of diffusion-weighted magnetic gradients,said predetermined arrangement of diffusion-weighted magnetic gradientschosen to:i) emphasize a selected structure in the region exhibitingdiffusion anisotropy in a particular direction; and ii) suppress otherstructures in the region exhibiting diffusion anisotropy in directionsdifferent from said particular direction; (c) for each of saiddiffusion-weighted gradients, sensing a resonant response of the regionto the diffusion-weighted gradient and producing an output indicative ofthe resonant response; and (d) processing said outputs to generate datarepresentative of the diffusion anisotropy of the selected structure.52. The method of claim 51, wherein said data representative of thediffusion anisotropy of the selected structure is processed to produce adata set that describes the shape and position of the selectedstructure.
 53. The method of claim 52, wherein the selected diffusionanisotropic structure is neural tissue in vivo and living.
 54. Amagnetic resonance apparatus for determining data representative of thediffusion anisotropy exhibited by a structure, said apparatusincluding:(a) excitation and output arrangement means for exposing aregion to a suppression sequence of electromagnetic fields thatsuppresses the electromagnetic responsiveness of structures in theregion that do not exhibit diffusion anisotropy, so as to increase theapparent diffusion anisotropy of structures in the region that exhibitdiffusion anisotropy, said suppression sequence of electromagneticfields not including diffusion-weighted magnetic gradients; (b)polarizing field source means positioned near said excitation and outputarrangement means for exposing the region to a predetermined arrangementof diffusion-weighted magnetic gradients chosen to:i) emphasize aselected structure in the region exhibiting diffusion anisotropy in aparticular direction; and ii) suppress other structures in the regionexhibiting diffusion anisotropy in directions different from saidparticular direction, said excitation and output arrangement meansfurther for sensing a resonant response of the region to thediffusion-weighted gradient and producing an output indicative of theresonant response, for each of said diffusion-weighted gradients; and(c) processor means coupled to said excitation and output arrangementmeans for processing said outputs to generate data representative of thediffusion anisotropy of the selected structure.
 55. A magnetic resonanceapparatus for determining the shape and position of a structure, saidapparatus including:(a) polarizing field source means for exposing aregion to a magnetic polarizing field including a predeterminedarrangement of diffusion-weighted gradients, the region including aselected structure that exhibits diffusion anisotropy and otherstructures that do not exhibit diffusion anisotropy; (b) excitation andoutput arrangement means positioned near said polarizing field sourcemeans for:i) exposing the region to an electromagnetic excitation field;and ii) for each of said diffusion-weighted gradients, sensing aresonant response of the region to the excitation field and thepolarizing field including the diffusion-weighted gradient and producingan output indicative of the resonant response; and (c) processor meanscoupled to said excitation and output arrangement means for:i) vectorprocessing said outputs to generate data representative of anisotropicdiffusion exhibited by the selected structure in the region, regardlessof the alignment of said diffusion-weighted gradients with respect tothe orientation of said selected structure; and ii) processing said datarepresentative of anisotropic diffusion to generate a data setdescribing the shape and position of said selected structure in theregion, said data set distinguishing said selected structure from otherstructures in the region that do not exhibit diffusion anisotropy. 56.The apparatus of claim 55, wherein said selected structure is neuraltissue in a mammal and said other structures are non-neural tissue inthe mammal.
 57. The apparatus of claim 56, wherein:said processor meansis further for analyzing said data representative of anisotropicdiffusion to determine an effective direction of the anisotropicdiffusion exhibited by said neural tissue, so as to determine an optimalorientation for diffusion-weighted gradients; said polarizing fieldsource means is further for exposing the region to two additionaldiffusion-weighted gradients respectively substantially parallel to andsubstantially perpendicular to said effective direction; said excitationand output arrangement means is further for producing two additionaloutputs indicative of the region's resonant responses respectively tosaid two additional diffusion-weighted gradients; and said processormeans is further for determining the difference between said twoadditional outputs to generate said data set describing the shape andposition of said neural tissue.
 58. The apparatus of claim 56, whereinsaid data set describing the shape and position of said neural tissuedescribes the shape and position of a selected cross section of saidneural tissue, and said apparatus is further for generating additionaldata sets describing different cross sections of said neural tissue, andsaid processor means is further for calculating a further data set thatdescribes the three dimensional shape and position of a segment of saidneural tissue by:analyzing the data representative of anisotropicdiffusion to determine how to relate said data set and said additionaldata sets describing the shape and position of cross sections of saidneural tissue; and based upon the results of said analyzing the datarepresentative of anisotropic diffusion, combining said data set andsaid additional data sets to generate said further data set thatdescribes the three dimensional shape and position of the segment ofsaid neural tissue, thereby allowing a three dimensional shape andposition of curved neural tissue to be described.
 59. The apparatus ofclaim 56, wherein said predetermined arrangement of gradients includesfirst, second, and third orthogonal gradients, and said datarepresentative of anisotropic diffusion include a description of aneffective vector representative of the anisotropic diffusion exhibitedby said neural tissue.
 60. The apparatus of claim 55, wherein:saidprocessor means is further for analyzing said data representative ofanisotropic diffusion to determine an effective direction of theanisotropic diffusion exhibited by said selected structure, so as todetermine an optimal orientation for diffusion-weighted gradients; saidpolarizing field source means is further for exposing the region to twoadditional diffusion-weighted gradients respectively substantiallyparallel to and substantially perpendicular to said effective direction;said excitation and output arrangement means is further for producingtwo additional outputs indicative of the region's resonant responsesrespectively to said two additional diffusion-weighted gradients; andsaid processor means is further for determining a difference betweensaid two additional outputs to generate said data set describing theshape and position of said selected structure.
 61. The apparatus ofclaim 55, wherein said data set describing the shape and position ofsaid selected structure describes the shape and position of a selectedcross section of said selected structure, and said apparatus is furtherfor generating additional data sets describing different cross sectionsof said selected structure, and said processor means is further fordetermining a further data set that describes the three dimensionalshape and position of a segment of said selected structure by:analyzingthe data representative of anisotropic diffusion to determine how torelate said data set and said additional data sets describing the shapeand position of cross sections of said selected structure; and basedupon the results of said analyzing the data representative ofanisotropic diffusion, combining said data set and said additional datasets to generate said further data set that describes the threedimensional shape and position of the segment of said selectedstructure, thereby enabling a three dimensional shape and position ofcurved structure exhibiting anisotropic diffusion to be described. 62.The apparatus of claim 55, wherein said predetermined arrangement ofgradients includes first, second, and third orthogonal gradients, andsaid data representative of anisotropic diffusion include a descriptionof an effective vector representative of the anisotropic diffusionexhibited by said selected structure.
 63. The method of claim 61,wherein the selected diffusion anisotropic structure is a member of thegroup consisting of peripheral nerves, cranial nerves numbers threethrough twelve, and autonomic nerves, and is living.
 64. The apparatusof claim 61, wherein said processor means is further for processing saiddata representative of the diffusion anisotropy of the selectedstructure to produce a data set that describes the shape and position ofthe selected structure.
 65. The apparatus of claim 64, wherein theselected diffusion anisotropic structure is neural tissue and is living.66. The apparatus of claim 64, wherein the selected diffusionanisotropic structure is a member of the group consisting of peripheralnerves, cranial nerves numbers three through twelve, and autonomicnerves, and is living.